def _set_lim_and_transforms(self): self.transAxes = BboxTransformTo(self.bbox) # Transforms the x and y axis separately by a scale factor # It is assumed that this part will have non-linear components self.transScale = TransformWrapper(IdentityTransform()) # A (possibly non-linear) projection on the (already scaled) # data. This one is aware of rmin self.transProjection = self.PolarTransform(self) # This one is not aware of rmin self.transPureProjection = self.PolarTransform() # An affine transformation on the data, generally to limit the # range of the axes self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim) # The complete data transformation stack -- from data all the # way to display coordinates self.transData = self.transScale + self.transProjection + \ (self.transProjectionAffine + self.transAxes) # This is the transform for theta-axis ticks. It is # equivalent to transData, except it always puts r == 1.0 at # the edge of the axis circle. self._xaxis_transform = (self.transPureProjection + self.PolarAffine( IdentityTransform(), Bbox.unit()) + self.transAxes) # The theta labels are moved from radius == 0.0 to radius == 1.1 self._theta_label1_position = Affine2D().translate(0.0, 1.1) self._xaxis_text1_transform = (self._theta_label1_position + self._xaxis_transform) self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1) self._xaxis_text2_transform = (self._theta_label2_position + self._xaxis_transform) # This is the transform for r-axis ticks. It scales the theta # axis so the gridlines from 0.0 to 1.0, now go from 0.0 to # 2pi. self._yaxis_transform = (Affine2D().scale(np.pi * 2.0, 1.0) + self.transData) # The r-axis labels are put at an angle and padded in the r-direction self._r_label1_position = ScaledTranslation( 22.5, self._rpad, blended_transform_factory(Affine2D(), BboxTransformToMaxOnly(self.viewLim))) self._yaxis_text1_transform = (self._r_label1_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform) self._r_label2_position = ScaledTranslation( 22.5, -self._rpad, blended_transform_factory(Affine2D(), BboxTransformToMaxOnly(self.viewLim))) self._yaxis_text2_transform = (self._r_label2_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform)
def add_letter(ax: Axes = None, offset: float = 0, offset2: float = 0, letter: str = None): """ add a letter indicating which subplot it is to the given figure """ global letter_index from matplotlib.transforms import Affine2D, ScaledTranslation # get the axes if ax is None: ax = plt.gca() # get the figure fig = ax.figure # get the font properties for figure letters font = get_letter_font_prop() # if no letter is given if letter is None: # use the letter_format from the font letter = font.letter_format # and add a letter given the current letter_index letter = letter.replace("a", chr(ord("a") + letter_index)) letter = letter.replace("A", chr(ord("A") + letter_index)) # increase the letter index letter_index += 1 # add a transform that gives the coordinates relative to the left top corner of the axes in cm transform = Affine2D().scale(1 / 2.54, 1 / 2.54) + fig.dpi_scale_trans + ScaledTranslation(0, 1, ax.transAxes) # add a text a the given position ax.text(-0.5+offset, offset2, letter, fontproperties=font, transform=transform, ha="center", va="bottom", picker=True)
def auto_shift(offset): """ Return a y-offset coordinate transform for the current axes. Each call to auto_shift increases the y-offset for the next line by the given number of points (with 72 points per inch). Example:: from matplotlib import pyplot as plt from bumps.plotutil import auto_shift trans = auto_shift(plt.gca()) plot(x, y, trans=trans) """ from matplotlib.transforms import ScaledTranslation import pylab ax = pylab.gca() if ax.lines and hasattr(ax, '_auto_shift'): ax._auto_shift += offset else: ax._auto_shift = 0 trans = pylab.gca().transData if ax._auto_shift: trans += ScaledTranslation(0, ax._auto_shift / 72., pylab.gcf().dpi_scale_trans) return trans
def setup_xticklabels(self): """Setup the xtick labels.""" # Labels are placed manually because this is around 25% faster than # using the minor ticks. xticks_info = self.make_xticks_info() self.ax1.set_xticks(xticks_info[0]) for i in range(len(self.xlabels)): self.xlabels[i].remove() padding = ScaledTranslation(0, -5 / 72, self.dpi_scale_trans) self.xlabels = [] for i in range(len(xticks_info[1])): new_label = self.ax1.text(xticks_info[1][i], 0, xticks_info[2][i], rotation=45, va='top', ha='right', fontsize=10, transform=self.ax1.transData + padding) self.xlabels.append(new_label)
def __init__(self, axes, helper, offset=None, axis_direction="bottom", **kw): """ *axes* : axes *helper* : an AxisArtistHelper instance. """ #axes is also used to follow the axis attribute (tick color, etc). super().__init__(**kw) self.axes = axes self._axis_artist_helper = helper if offset is None: offset = (0, 0) self.dpi_transform = Affine2D() self.offset_transform = ScaledTranslation(offset[0], offset[1], self.dpi_transform) self._label_visible = True self._majortick_visible = True self._majorticklabel_visible = True self._minortick_visible = True self._minorticklabel_visible = True #if self._axis_artist_helper._loc in ["left", "right"]: if axis_direction in ["left", "right"]: axis_name = "ytick" self.axis = axes.yaxis else: axis_name = "xtick" self.axis = axes.xaxis self._axisline_style = None self._axis_direction = axis_direction self._init_line() self._init_ticks(axis_name, **kw) self._init_offsetText(axis_direction) self._init_label() self.set_zorder(self.ZORDER) self._rotate_label_along_line = False # axis direction self._tick_add_angle = 180. self._ticklabel_add_angle = 0. self._axislabel_add_angle = 0. self.set_axis_direction(axis_direction)
def __init__(self, parent_axes=None, transform=None, coord_index=None, coord_type='scalar', coord_wrap=None, frame=None): # Keep a reference to the parent axes and the transform self.parent_axes = parent_axes self.transform = transform self.coord_index = coord_index self.coord_type = coord_type self.frame = frame if coord_type == 'longitude' and coord_wrap is None: self.coord_wrap = 360 elif coord_type != 'longitude' and coord_wrap is not None: raise NotImplementedError( 'coord_wrap is not yet supported for non-longitude coordinates' ) else: self.coord_wrap = coord_wrap # Initialize tick formatter/locator if coord_type == 'scalar': self._formatter_locator = ScalarFormatterLocator() elif coord_type in ['longitude', 'latitude']: self._formatter_locator = AngleFormatterLocator() else: raise ValueError( "coord_type should be one of 'scalar', 'longitude', or 'latitude'" ) # Initialize ticks self.dpi_transform = Affine2D() self.offset_transform = ScaledTranslation(0, 0, self.dpi_transform) self.ticks = Ticks(transform=parent_axes.transData + self.offset_transform) # Initialize tick labels self.ticklabels = TickLabels( transform=None, # display coordinates figure=parent_axes.get_figure()) # Initialize axis labels self.axislabels = AxisLabels( self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) # Initialize container for the grid lines self.grid_lines = [] self.grid_lines_kwargs = { 'visible': False, 'facecolor': 'none', 'transform': self.parent_axes.transData }
def __init__(self, parent_axes=None, parent_map=None, transform=None, coord_index=None, coord_type='scalar', coord_unit=None, coord_wrap=None, frame=None, format_unit=None): # Keep a reference to the parent axes and the transform self.parent_axes = parent_axes self.parent_map = parent_map self.transform = transform self.coord_index = coord_index self.coord_unit = coord_unit self.format_unit = format_unit self.frame = frame self.set_coord_type(coord_type, coord_wrap) # Initialize ticks self.dpi_transform = Affine2D() self.offset_transform = ScaledTranslation(0, 0, self.dpi_transform) self.ticks = Ticks(transform=parent_axes.transData + self.offset_transform) # Initialize tick labels self.ticklabels = TickLabels( self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) self.ticks.display_minor_ticks(rcParams['xtick.minor.visible']) self.minor_frequency = 5 # Initialize axis labels self.axislabels = AxisLabels( self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) # Initialize container for the grid lines self.grid_lines = [] # Initialize grid style. Take defaults from matplotlib.rcParams. # Based on matplotlib.axis.YTick._get_gridline. self.grid_lines_kwargs = { 'visible': False, 'facecolor': 'none', 'edgecolor': rcParams['grid.color'], 'linestyle': LINES_TO_PATCHES_LINESTYLE[rcParams['grid.linestyle']], 'linewidth': rcParams['grid.linewidth'], 'alpha': rcParams['grid.alpha'], 'transform': self.parent_axes.transData }
def __init__(self, axes, helper, offset=None, major_tick_size=None, major_tick_pad=None, minor_tick_size=None, minor_tick_pad=None, **kw): """ axes is also used to follow the axis attribute (tick color, etc). """ super(AxisArtist, self).__init__(**kw) self.axes = axes self._axis_artist_helper = helper if offset is None: offset = (0, 0) self.dpi_transform = Affine2D() self.offset_transform = ScaledTranslation(offset[0], offset[1], self.dpi_transform) self._label_visible = True self._majortick_visible = True self._majorticklabel_visible = True self._minortick_visible = True self._minorticklabel_visible = True if self._axis_artist_helper.label_direction in ["left", "right"]: axis_name = "ytick" self.axis = axes.yaxis else: axis_name = "xtick" self.axis = axes.xaxis if major_tick_size is None: self.major_tick_size = rcParams['%s.major.size' % axis_name] if major_tick_pad is None: self.major_tick_pad = rcParams['%s.major.pad' % axis_name] if minor_tick_size is None: self.minor_tick_size = rcParams['%s.minor.size' % axis_name] if minor_tick_pad is None: self.minor_tick_pad = rcParams['%s.minor.pad' % axis_name] self._axisline_style = None self._init_line() self._init_ticks() self._init_offsetText(self._axis_artist_helper.label_direction) self._init_label() self.set_zorder(self.ZORDER) self._rotate_label_along_line = False
def draw_figure_title(self): """Draw the title of the figure.""" labelDB = LabelDatabase(self.language) if self.isGraphTitle: # Set the text and position of the title. if self.meteo_on: offset = ScaledTranslation(0, 7/72, self.dpi_scale_trans) self.text1.set_text( labelDB.station_meteo % (self.name_meteo, self.dist)) self.text1.set_transform(self.ax0.transAxes + offset) dy = 30 if self.meteo_on else 7 offset = ScaledTranslation(0, dy/72, self.dpi_scale_trans) self.figTitle.set_text(labelDB.title % self.wldset['Well']) self.figTitle.set_transform(self.ax0.transAxes + offset) # Set whether the title is visible or not. self.text1.set_visible(self.meteo_on and self.isGraphTitle) self.figTitle.set_visible(self.isGraphTitle)
def __init__(self, parent_axes=None, parent_map=None, transform=None, coord_index=None, coord_type='scalar', coord_unit=None, coord_wrap=None, frame=None): # Keep a reference to the parent axes and the transform self.parent_axes = parent_axes self.parent_map = parent_map self.transform = transform self.coord_index = coord_index self.coord_unit = coord_unit self.frame = frame self.set_coord_type(coord_type, coord_wrap) # Initialize ticks self.dpi_transform = Affine2D() self.offset_transform = ScaledTranslation(0, 0, self.dpi_transform) self.ticks = Ticks(transform=parent_axes.transData + self.offset_transform) # Initialize tick labels self.ticklabels = TickLabels(self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) self.ticks.display_minor_ticks(False) self.minor_frequency = 5 # Initialize axis labels self.axislabels = AxisLabels(self.frame, transform=None, # display coordinates figure=parent_axes.get_figure()) # Initialize container for the grid lines self.grid_lines = [] # Initialize grid style. Take defaults from matplotlib.rcParams. # Based on matplotlib.axis.YTick._get_gridline. # # Matplotlib's gridlines use Line2D, but ours use PathPatch. # Patches take a slightly different format of linestyle argument. lines_to_patches_linestyle = {'-': 'solid', '--': 'dashed', '-.': 'dashdot', ':': 'dotted', 'none': 'none', 'None': 'none', ' ': 'none', '': 'none'} self.grid_lines_kwargs = {'visible': False, 'facecolor': 'none', 'edgecolor': rcParams['grid.color'], 'linestyle': lines_to_patches_linestyle[rcParams['grid.linestyle']], 'linewidth': rcParams['grid.linewidth'], 'alpha': rcParams.get('grid.alpha', 1.0), 'transform': self.parent_axes.transData}
def __init__(self, axes, helper, offset=None, axis_direction="bottom", **kwargs): """ Parameters ---------- axes : `mpl_toolkits.axisartist.axislines.Axes` helper : `~mpl_toolkits.axisartist.axislines.AxisArtistHelper` """ #axes is also used to follow the axis attribute (tick color, etc). super().__init__(**kwargs) self.axes = axes self._axis_artist_helper = helper if offset is None: offset = (0, 0) self.offset_transform = ScaledTranslation( *offset, Affine2D().scale(1 / 72) # points to inches. + self.axes.figure.dpi_scale_trans) if axis_direction in ["left", "right"]: axis_name = "ytick" self.axis = axes.yaxis else: axis_name = "xtick" self.axis = axes.xaxis self._axisline_style = None self._axis_direction = axis_direction self._init_line() self._init_ticks(axis_name, **kwargs) self._init_offsetText(axis_direction) self._init_label() self.set_zorder(self.ZORDER) self._rotate_label_along_line = False # axis direction self._tick_add_angle = 180. self._ticklabel_add_angle = 0. self._axislabel_add_angle = 0. self.set_axis_direction(axis_direction)
def plotEllipse(ax, x, y, semi_major, semi_minor, angle=0, fill=False, lw=3, log=False, **kwargs): from matplotlib.patches import Ellipse from matplotlib.transforms import ScaledTranslation if log: # use the axis scale tform to figure out how far to translate circ_offset = ScaledTranslation(x, y, ax.transScale) # construct the composite tform circ_tform = circ_offset + ax.transLimits + ax.transAxes # create the circle centred on the origin, apply the composite tform circ = Ellipse((0, 0), 2 * semi_major, 2 * semi_minor, angle=angle, fill=fill, lw=lw, transform=circ_tform, **kwargs) ax.add_artist(circ) else: return ax.add_artist( Ellipse((x, y), semi_major, semi_minor, angle=angle, fill=fill, lw=lw, **kwargs))
def setNode(self, node): if self.artist_ != None: self.artist_[0].remove() self.artist_[1].remove() if self.parent_ == None: return [m11, m12, m13, m21, m22, m23] = node.getTransformation() #https://matplotlib.org/3.1.0/tutorials/advanced/transforms_tutorial.html mtx = np.array([[m11, m12, 0], [m21, m22, 0], [0, 0, 1]]) refcs = Affine2D(matrix=mtx) xx, xy = refcs.transform((0.0, 1.0)) yx, yy = refcs.transform((1.0, 0.0)) self.ox_ = m13 self.oy_ = m23 axes = self.parent_.axes_ figure = self.parent_.fig_ trans = (figure.dpi_scale_trans + ScaledTranslation(self.ox_, self.oy_, axes.transData)) cx = axes.arrow(0, 0, xx, xy, head_width=0.1, head_length=0.2, fc='b', ec='b', transform=trans) cy = axes.arrow(0, 0, yx, yy, head_width=0.1, head_length=0.2, fc='r', ec='r', transform=trans) self.artist_ = (cx, cy)
def plot_legend(self): """Plot the legend of the figure.""" ax = self.figure.axes[2] # bbox transform : padding = ScaledTranslation(5 / 72, -5 / 72, self.figure.dpi_scale_trans) transform = ax.transAxes + padding # Define proxy artists : colors = ColorsReader() colors.load_colors_db() rec1 = Rectangle((0, 0), 1, 1, fc=colors.rgb['Snow'], ec='none') rec2 = Rectangle((0, 0), 1, 1, fc=colors.rgb['Rain'], ec='none') # Define the legend labels and markers : lines = [ax.lines[0], ax.lines[1], ax.lines[2], rec2, rec1] labels = [ self.fig_labels.Tmax, self.fig_labels.Tavg, self.fig_labels.Tmin, self.fig_labels.rain, self.fig_labels.snow ] # Plot the legend : leg = ax.legend(lines, labels, numpoints=1, fontsize=13, borderaxespad=0, loc='upper left', borderpad=0, bbox_to_anchor=(0, 1), bbox_transform=transform) leg.draw_frame(False)
def generate_hydrograph(self, wxdset=None, wldset=None): wxdset = self.wxdset if wxdset is None else wxdset wldset = self.wldset if wldset is None else wldset # Reinit the figure. self.clf() self.set_size_inches(self.fwidth, self.fheight, forward=True) self.setup_figure_frame() # Assign Weather Data. if self.wxdset is None: self.name_meteo = '' self.TIMEmeteo = np.array([]) self.TMAX = np.array([]) self.PTOT = np.array([]) self.RAIN = np.array([]) else: self.name_meteo = wxdset.metadata['Station Name'] self.TIMEmeteo = datetimeindex_to_xldates(wxdset.data.index) self.TMAX = wxdset.data['Tmax'].values self.PTOT = wxdset.data['Ptot'].values self.RAIN = wxdset.data['Rain'].values # Resample Data in Bins : self.resample_bin() # -------------------------------------------------- AXES CREATION ---- # ---- Time (host) ---- # Also holds the gridlines. self.ax1 = self.add_axes([0, 0, 1, 1], frameon=False) self.ax1.set_zorder(100) # ---- Frame ---- # Only used to display the frame so it is always on top. self.ax0 = self.add_axes(self.ax1.get_position(), frameon=True) self.ax0.patch.set_visible(False) self.ax0.set_zorder(self.ax1.get_zorder() + 200) self.ax0.tick_params(bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False) # ---- Water Levels ---- self.ax2 = self.add_axes(self.ax1.get_position(), frameon=False, label='axes2', sharex=self.ax1) self.ax2.set_zorder(self.ax1.get_zorder() + 100) self.ax2.yaxis.set_ticks_position('left') self.ax2.yaxis.set_label_position('left') self.ax2.tick_params(axis='y', direction='out', labelsize=10) # ---- Precipitation ---- self.ax3 = self.add_axes(self.ax1.get_position(), frameon=False, label='axes3', sharex=self.ax1) self.ax3.set_zorder(self.ax1.get_zorder() + 150) self.ax3.set_navigate(False) # ---- Air Temperature ---- self.ax4 = self.add_axes(self.ax1.get_position(), frameon=False, label='axes4', sharex=self.ax1) self.ax4.set_zorder(self.ax1.get_zorder() + 150) self.ax4.set_navigate(False) self.ax4.set_axisbelow(True) self.ax4.tick_params(bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False) if self.meteo_on is False: self.ax3.set_visible(False) self.ax4.set_visible(False) # ---- Bottom Graph Grid ---- self.axLow = self.add_axes(self.ax1.get_position(), frameon=False, label='axLow', sharex=self.ax1) self.axLow.patch.set_visible(False) self.axLow.set_zorder(self.ax2.get_zorder() - 50) self.axLow.tick_params(bottom=False, top=False, left=False, right=False, labelbottom=False, labelleft=False) self.setup_waterlvl_scale() # -------------------------------------------------- Remove Spines ---- for axe in self.axes[2:]: for loc in axe.spines: axe.spines[loc].set_visible(False) # ------------------------------------------------- Update margins ---- self.bottom_margin = 0.75 self.set_margins() # set margins for all the axes # --------------------------------------------------- FIGURE TITLE ---- # Calculate horizontal distance between weather station and # observation well. if self.wxdset is not None: self.dist = calc_dist_from_coord( wldset['Latitude'], wldset['Longitude'], wxdset.metadata['Latitude'], wxdset.metadata['Longitude']) else: self.dist = 0 # Weather Station name and distance to the well self.text1 = self.ax0.text(0, 1, '', va='bottom', ha='left', rotation=0, fontsize=10) # Well Name self.figTitle = self.ax0.text(0, 1, '', fontsize=18, ha='left', va='bottom') self.draw_figure_title() # ----------------------------------------------------------- TIME ---- self.xlabels = [] self.set_time_scale() self.ax1.xaxis.set_ticklabels([]) self.ax1.xaxis.set_ticks_position('bottom') self.ax1.tick_params(axis='x', direction='out') self.ax1.tick_params(top=False, left=False, right=False, labeltop=False, labelleft=False, labelright=False) self.set_gridLines() # ---- Init water level artists # Continuous Line Datalogger self.l1_ax2, = self.ax2.plot( [], [], '-', zorder=10, lw=1, color=self.colorsDB.rgb['WL solid']) # Data Point Datalogger self.l2_ax2, = self.ax2.plot( [], [], '.', color=self.colorsDB.rgb['WL data'], markersize=5) # Manual Mesures self.h_WLmes, = self.ax2.plot( [], [], 'o', zorder=15, label='Manual measures', markerfacecolor='none', markersize=5, markeredgewidth=1.5, mec=self.colorsDB.rgb['WL obs']) # Predicted Recession Curves self._mrc_plt, = self.ax2.plot( [], [], color='red', lw=1.5, dashes=[5, 3], zorder=100, alpha=0.85) # Predicted GLUE water levels self.glue_plt, = self.ax2.plot([], []) self.draw_waterlvl() self.draw_glue_wl() self.draw_mrc_wl() # ---- Init weather artists # ---- PRECIPITATION ----- self.ax3.yaxis.set_ticks_position('right') self.ax3.yaxis.set_label_position('right') self.ax3.tick_params(axis='y', direction='out', labelsize=10) self.PTOT_bar, = self.ax3.plot([], []) self.RAIN_bar, = self.ax3.plot([], []) self.baseline, = self.ax3.plot( [self.TIMEmin, self.TIMEmax], [0, 0], 'k') # ---- AIR TEMPERATURE ----- TEMPmin = -40 TEMPscale = 20 TEMPmax = 40 self.ax4.axis(ymin=TEMPmin, ymax=TEMPmax) yticks_position = np.array([TEMPmin, 0, TEMPmax]) yticks_position = np.arange( TEMPmin, TEMPmax + TEMPscale / 2, TEMPscale) self.ax4.set_yticks(yticks_position) self.ax4.yaxis.set_ticks_position('left') self.ax4.tick_params(axis='y', direction='out', labelsize=10) self.ax4.yaxis.set_label_position('left') self.ax4.set_yticks([-20, 20], minor=True) self.ax4.tick_params(axis='y', which='minor', length=0) self.ax4.xaxis.set_ticklabels([], minor=True) self.l1_ax4, = self.ax4.plot([], []) # fill shape self.l2_ax4, = self.ax4.plot( [], [], color='black', lw=1) # contour line # ---- MISSING VALUES MARKERS # Precipitation. vshift = 5 / 72 offset = ScaledTranslation(0, vshift, self.dpi_scale_trans) if self.wxdset is not None: t1 = pd.DataFrame(self.wxdset.missing_value_indexes['Ptot'], index=self.wxdset.missing_value_indexes['Ptot'], columns=['datetime']) t2 = pd.DataFrame(self.wxdset.missing_value_indexes['Ptot'] + pd.Timedelta('1 days'), self.wxdset.missing_value_indexes['Ptot'] + pd.Timedelta('1 days'), columns=['datetime']) time = datetimeindex_to_xldates(pd.DatetimeIndex( pd.concat([t1, t2], axis=0) .drop_duplicates() .resample('1D') .asfreq() ['datetime'] )) y = np.ones(len(time)) * self.ax4.get_ylim()[0] else: time, y = [], [] self.lmiss_ax4, = self.ax4.plot( time, y, ls='-', solid_capstyle='projecting', lw=1, c='red', transform=self.ax4.transData + offset) # Air Temperature. offset = ScaledTranslation(0, -vshift, self.dpi_scale_trans) if self.wxdset is not None: t1 = pd.DataFrame(self.wxdset.missing_value_indexes['Tmax'], index=self.wxdset.missing_value_indexes['Tmax'], columns=['datetime']) t2 = pd.DataFrame(self.wxdset.missing_value_indexes['Tmax'] + pd.Timedelta('1 days'), self.wxdset.missing_value_indexes['Tmax'] + pd.Timedelta('1 days'), columns=['datetime']) time = datetimeindex_to_xldates(pd.DatetimeIndex( pd.concat([t1, t2], axis=0) .drop_duplicates() .resample('1D') .asfreq() ['datetime'] )) y = np.ones(len(time)) * self.ax4.get_ylim()[1] else: time, y = [], [] self.ax4.plot(time, y, ls='-', solid_capstyle='projecting', lw=1., c='red', transform=self.ax4.transData + offset) self.draw_weather() self.draw_ylabels() self.setup_legend() self.__isHydrographExists = True
def fnoise_plots(mode,ifeed): filelist = np.loadtxt(sys.argv[1],dtype=str) obsid = np.array([int(f.split('-')[1]) for f in filelist]) filelist = filelist[np.argsort(obsid)] obsid = np.sort(obsid) fnoise = np.zeros(filelist.size) enoise = np.zeros(filelist.size) feed = None isfg4 = np.zeros(filelist.size,dtype=bool) dist = np.zeros(filelist.size) fnoise_power = np.zeros((filelist.size,64*4)) alphas = np.zeros((filelist.size,64*4)) for ifile, filename in enumerate(filelist): try: data = h5py.File(filename,'r') except OSError: print('{} cannot be opened (Resource unavailable)'.format(filename)) fnoise[ifile] = np.nan if mode.lower() in data['level1/comap'].attrs['source'].decode('utf-8').lower(): isfg4[ifile] = True try: fits = data['level2/fnoise_fits'][ifeed,:,:,:] fnoise[ifile] = np.median(fits[:,1]) enoise[ifile] = np.sqrt(np.median(np.abs(fits[:,1]-fnoise[ifile])**2))*1.4826 ps = data['level2/powerspectra'][ifeed,:,:,:] rms = data['level2/wnoise_auto'][ifeed,:,:,:] nu = data['level2/freqspectra'][ifeed,:,:,:] freq = data['level1/spectrometer/frequency'][...] bw = 16 freq = np.mean(np.reshape(freq, (freq.shape[0],freq.shape[1]//bw, bw)),axis=-1).flatten() sfreq = np.argsort(freq) fnoise_power[ifile,:] = (rms[:,:,0]**2 * (1/fits[:,:,0])**fits[:,:,1]).flatten()[sfreq] alphas[ifile,:] = (fits[:,:,1]).flatten()[sfreq] #print(nu.shape,ps.shape, rms.shape, fits.shape) #pyplot.plot(freq[sfreq],fnoise_power[ifile,:]) except IOError: print('{} not processed'.format(filename.split('/')[-1])) fnoise[ifile] = np.nan if isinstance(feed, type(None)): feed = data['level1/spectrometer/feeds'][ifeed] # Calculate sun distance mjd = data['level1/spectrometer/MJD'][0:1] lon=-118.2941 lat=37.2314 ra_sun, dec_sun, raddist = Coordinates.getPlanetPosition('SUN', lon, lat, mjd) az_sun, el_sun = Coordinates.e2h(ra_sun, dec_sun, mjd, lon, lat) ra = data['level1/spectrometer/pixel_pointing/pixel_ra'][0,0:1] dec = data['level1/spectrometer/pixel_pointing/pixel_dec'][0,0:1] dist[ifile] = el_sun[0]#angular_seperation(ra_sun, ra, dec_sun, dec) data.close() # Plot obs ID vs fnoise power pyplot.imshow(np.log10(fnoise_power*1e3),aspect='auto',origin='lower', extent=[np.min(freq),np.max(freq),-.5,fnoise_power.shape[0]-0.5]) pyplot.yticks(np.arange(fnoise_power.shape[0])-0.5, obsid, rotation=0, ha='right',va='center',size=10) ax = pyplot.gca() fig = pyplot.gcf() offset = ScaledTranslation(-0.08,0.02,fig.transFigure) for label in ax.yaxis.get_majorticklabels(): label.set_transform(label.get_transform() + offset) pyplot.grid() pyplot.xlabel('Frequency (GHz)') pyplot.ylabel('obs ID') pyplot.colorbar(label=r'$\mathrm{log}_{10}$(mK)') pyplot.title('Feed {}'.format(feed)) pyplot.savefig('Plots/fnoise_gfields_Feed{}.png'.format(feed),bbox_inches='tight') pyplot.clf() # Plot obs ID vs fnoise power pyplot.imshow(alphas,aspect='auto',origin='lower',vmin=-1.5,vmax=-0.9, extent=[np.min(freq),np.max(freq),-.5,fnoise_power.shape[0]-0.5]) pyplot.yticks(np.arange(fnoise_power.shape[0])-0.5, obsid, rotation=0, ha='right',va='center',size=10) ax = pyplot.gca() fig = pyplot.gcf() for label in ax.yaxis.get_majorticklabels(): label.set_transform(label.get_transform() + offset) pyplot.grid() pyplot.xlabel('Frequency (GHz)') pyplot.ylabel('obs ID') pyplot.colorbar(label=r'$\alpha$') pyplot.title('Feed {}'.format(feed)) pyplot.savefig('Plots/alphas_gfields_Feed{}.png'.format(feed),bbox_inches='tight') pyplot.clf()
########## simulations without binding ########## plt.figure("0bind", figsize=figsize) a, t, ood = fields.get("eventspara_nobind", "a", "t", "ood") a, t, success = np.array(a), np.array(t), np.array(ood) == 0 plt.scatter(t[~success], a[~success], **s_fail) plt.scatter(t[success], a[success], **s_success) plt.scatter(t_exp, a_exp, **s_experiment) ax = plt.gca() format_ticks(ax) plt.legend(loc="lower left", scatterpoints=3, frameon=False, borderaxespad=0.2, handletextpad=0.4) # encircle long bindings offset = ScaledTranslation(18, 27, ax.transScale) tform = offset + ax.transLimits + ax.transAxes ell = mpatch.Ellipse((0, 0), 1.8, 10, angle=0.0, transform=tform, fc="None", ec=long_bind_color) ax.add_patch(ell) plt.text(15, 21, "Assumed\n long bindings", color=long_bind_color, horizontalalignment="center") ########## simulations with one binding ########## plt.figure("1bind", figsize=figsize) a, t, ood = fields.get("eventsnew_onlyone_2_", "a", "t", "ood") a, t, success = np.array(a), np.array(t), np.array(ood) == 0 plt.scatter(t[~success], a[~success], **s_fail) plt.scatter(t[success], a[success], **s_success) plt.scatter(t_exp, a_exp, **s_experiment)
def __init__(self, lang='English'): super(BRFFigure, self).__init__() lang = lang if lang.lower() in FigureLabels.LANGUAGES else 'English' self.__figlang = lang self.__figlabels = FigureLabels(lang) # ---- Figure Creation fig_width = 8 fig_height = 5 self.set_size_inches(fig_width, fig_height) self.patch.set_facecolor('white') left_margin = 0.8 right_margin = 0.25 bottom_margin = 0.75 top_margin = 0.25 # ---- Axe Setup ax = self.add_axes([ left_margin / fig_width, bottom_margin / fig_height, 1 - (left_margin + right_margin) / fig_width, 1 - (bottom_margin + top_margin) / fig_height ], zorder=1) ax.set_visible(False) # ---- Ticks Setup ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') ax.tick_params(axis='both', which='major', direction='out', gridOn=True) # ---- Artists Init self.line, = ax.plot([], [], ls='-', color='blue', linewidth=1.5, zorder=20, clip_on=True) self.markers, = ax.plot([], [], color='0.1', mec='0.1', marker='.', ls='None', ms=5, zorder=30, mew=1, clip_on=False) self.errbar, = ax.plot([], []) offset = ScaledTranslation(0, -5 / 72, self.dpi_scale_trans) self.title = ax.text(0.5, 1, '', ha='center', va='top', fontsize=14, transform=ax.transAxes + offset)
def _T(self): F = self.plotter.figure.dpi_scale_trans S = ScaledTranslation(self.point[0], self.point[1], self.plotter.axes.transData) T = F + S return T
def transform_non_affine(self, values): x, y = self._get_translation() trans = ScaledTranslation(x, y, self.figure.dpi_scale_trans) return trans.transform(values)
import numpy as np import matplotlib.pyplot as plt from matplotlib.transforms import blended_transform_factory, ScaledTranslation fig = plt.figure(figsize=(6, 4)) ax = fig.add_subplot(1, 1, 1, aspect=1) ax.set_xlim(0, 10) ax.set_xticks(range(11)) ax.set_ylim(0, 5) ax.set_xticks(range(11)) point = 1 / 72 fontsize = 12 dx, dy = 0, -1.5 * fontsize * point offset = ScaledTranslation(dx, dy, fig.dpi_scale_trans) transform = blended_transform_factory(ax.transData, ax.transAxes + offset) for x in range(11): plt.text(x, 0, "↑", transform=transform, ha="center", va="top", fontsize=fontsize) plt.tight_layout() plt.savefig("../../figures/coordinates/transforms-blend.pdf") plt.show()
def __init__(self, lang='English'): lang = lang if lang.lower() in FigureLabels.LANGUAGES else 'English' self.__figlang = lang self.__figlabels = FigureLabels(lang) self.normals = None fw, fh = 8.5, 5. fig = MplFigure(figsize=(fw, fh), facecolor='white') super(FigWeatherNormals, self).__init__(fig) # Define the margins. left_margin = 1 / fw right_margin = 1 / fw bottom_margin = 0.7 / fh top_margin = 0.1 / fh # ---- Yearly Avg Labels # The yearly yearly averages for the mean air temperature and # the total precipitation are displayed in <ax3>, which is placed on # top of the axes that display the data (<ax0> and <ax1>). ax3 = fig.add_axes([0, 0, 1, 1], zorder=1, label='axe3') ax3.patch.set_visible(False) ax3.spines['bottom'].set_visible(False) ax3.tick_params(axis='both', bottom=False, top=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False) # Mean Annual Air Temperature : # Places first label at the top left corner of <ax3> with a horizontal # padding of 5 points and downward padding of 3 points. dx, dy = 5 / 72., -3 / 72. padding = ScaledTranslation(dx, dy, fig.dpi_scale_trans) transform = ax3.transAxes + padding ax3.text(0., 1., 'Mean Annual Air Temperature', fontsize=13, va='top', transform=transform) # Mean Annual Precipitation : # Get the bounding box of the first label. renderer = self.get_renderer() bbox = ax3.texts[0].get_window_extent(renderer) bbox = bbox.transformed(ax3.transAxes.inverted()) # Places second label below the first label with a horizontal # padding of 5 points and downward padding of 3 points. ax3.text(0., bbox.y0, 'Mean Annual Precipitation', fontsize=13, va='top', transform=transform) bbox = ax3.texts[1].get_window_extent(renderer) bbox = bbox.transformed(fig.transFigure.inverted()) # Update geometry : # Updates the geometry and position of <ax3> to accomodate the text. x0 = left_margin axw = 1 - (left_margin + right_margin) axh = 1 - bbox.y0 - (dy / fw) y0 = 1 - axh - top_margin ax3.set_position([x0, y0, axw, axh]) # ---- Data Axes axh = y0 - bottom_margin y0 = y0 - axh # Precipitation : ax0 = fig.add_axes([x0, y0, axw, axh], zorder=1, label='axe0') ax0.patch.set_visible(False) ax0.spines['top'].set_visible(False) ax0.set_axisbelow(True) # Air Temperature : ax1 = fig.add_axes(ax0.get_position(), frameon=False, zorder=5, sharex=ax0, label='axe1') # ---- Initialize the Artists # This is only to initiates the artists and to set their parameters # in advance. The plotting of the data is actually done by calling # the <plot_monthly_normals> method. XPOS = np.arange(-0.5, 12.51, 1) XPOS[0] = 0 XPOS[-1] = 12 y = range(len(XPOS)) colors = ['#990000', '#FF0000', '#FF6666'] # Dashed lines for Tmax, Tavg, and Tmin : for i in range(3): ax1.plot(XPOS, y, color=colors[i], ls='--', lw=1.5, zorder=100) # Markers for Tavg : ax1.plot(XPOS[1:-1], y[1:-1], color=colors[1], marker='o', ls='none', ms=6, zorder=100, mec=colors[1], mfc='white', mew=1.5) # ---- Xticks Formatting Xmin0 = 0 Xmax0 = 12.001 # Major ticks ax0.xaxis.set_ticks_position('bottom') ax0.tick_params(axis='x', direction='out') ax0.xaxis.set_ticklabels([]) ax0.set_xticks(np.arange(Xmin0, Xmax0)) ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) # Minor ticks ax0.set_xticks(np.arange(Xmin0 + 0.5, Xmax0 + 0.49, 1), minor=True) ax0.tick_params(axis='x', which='minor', direction='out', length=0, labelsize=13) ax0.xaxis.set_ticklabels(self.fig_labels.month_names, minor=True) # ---- Y-ticks Formatting # Precipitation ax0.yaxis.set_ticks_position('right') ax0.tick_params(axis='y', direction='out', labelsize=13) ax0.tick_params(axis='y', which='minor', direction='out') ax0.yaxis.set_ticklabels([], minor=True) # Air Temperature ax1.yaxis.set_ticks_position('left') ax1.tick_params(axis='y', direction='out', labelsize=13) ax1.tick_params(axis='y', which='minor', direction='out') ax1.yaxis.set_ticklabels([], minor=True) # ---- Grid Parameters # ax0.grid(axis='y', color=[0.5, 0.5, 0.5], linestyle=':', linewidth=1, # dashes=[1, 5]) # ax0.grid(axis='y', color=[0.75, 0.75, 0.75], linestyle='-', # linewidth=0.5) # ---- Limits of the Axes ax0.set_xlim(Xmin0, Xmax0) # ---- Legend self.plot_legend()
def plotGenomeTracks(tracks, chromosome, coordStart, coordEnd, style='ggplot', outpath=None, figsize=None): # TODO Refactor into separate functions and classes if figsize is None: figsize = (13., 1. * len(tracks)) def coordFmt(t): if t >= 1000000: return '%.3f Mb' % (t / 1000000.) if t >= 1000: return '%.3f kb' % (t / 1000.) return '%.3f bp' % t try: import matplotlib.pyplot as plt import base64 from io import BytesIO from IPython.core.display import display, HTML from matplotlib.collections import PatchCollection from matplotlib.patches import Rectangle, FancyBboxPatch, Polygon from matplotlib.lines import Line2D from matplotlib.transforms import ScaledTranslation import struct with plt.style.context(style): fig, ax = plt.subplots(1, figsize=figsize) # Set up axes plt.yticks(range(len(tracks))) ax.set_xlim(coordStart, coordEnd) ax.set_ylim(0, len(tracks)) ticks = ax.get_xticks() ax.set_xticklabels([coordFmt(t) for t in ticks], fontsize=12) ax.set_yticklabels([]) setYA = [] setYB = [] width = coordEnd - coordStart y = 0. for rs in tracks: yA = y if isinstance(rs, genome): y += 3. elif isinstance(rs, curves): y += 3. if rs.thresholdValue is not None: y += 1. else: y += 1. setYA.append(yA) setYB.append(y) plt.text(coordStart - width * 0.015, (y + yA) / 2. + 0.05, rs.name, verticalalignment='top', horizontalalignment='right', color=ax.yaxis.label.get_color(), fontsize=15, rotation=45, clip_on=False) ax.set_yticks([0.] + setYB) ax.yaxis.set_label_coords(-0.3, 1.5) plt.setp(ax.get_yticklabels(), rotation=45, ha="right", rotation_mode="anchor") plt.tick_params(axis='y', which='both', left=False, right=False) # Shift y axis labels dx = -0.1 dy = 0.2 offset = ScaledTranslation(dx, dy, fig.dpi_scale_trans) for label in ax.yaxis.get_majorticklabels(): label.set_transform(label.get_transform() + offset) # Iterate over tracks rmargin = 0.2 for rsi, ((yA, yB), rs) in enumerate(zip(zip(setYA, setYB), tracks)): # Special treatment of gene annotations, with plotting of genes with exons, CDS and names height = yB - yA colorRaw = plt.rcParams['axes.prop_cycle'].by_key()['color'][ rsi % len(plt.rcParams['axes.prop_cycle'].by_key()['color'])] color = [ v / 255. for v in struct.unpack('BBB', bytes.fromhex(colorRaw[1:])) ] def drawRoundBox(start, end, y, height, fc, ec, rlabel=None, clip_on=True): w = end - start + 1 rs = min(w * 0.5 * width * 0.00001, 500. * width * 0.00001) patch = FancyBboxPatch( (start, y), w, height, boxstyle="round,rounding_size=" + str(rs), mutation_aspect=2. * max(setYB) / width, fc=fc, ec=ec, clip_on=clip_on) ax.add_patch(patch) if rlabel is not None: plt.text(end + width * 0.015, y + height * .5, rlabel, verticalalignment='center', horizontalalignment='left', color=ax.yaxis.label.get_color(), fontsize=10, clip_on=False) return patch def drawBox(start, end, y, height, fc, ec, rlabel=None, clip_on=True): w = end - start + 1 rs = min(w * width * 0.00001, 500. * width * 0.00001) patch = FancyBboxPatch( (start, y), w, height, boxstyle="square", mutation_aspect=2. * max(setYB) / width, fc=fc, ec=ec, clip_on=clip_on) ax.add_patch(patch) if rlabel is not None: plt.text(end + width * 0.015, y + height * .5, rlabel, verticalalignment='center', horizontalalignment='left', color=ax.yaxis.label.get_color(), fontsize=10, clip_on=False) return patch if isinstance(rs, genome): cgenes = [ g for g in rs.genes if g.region.seq == chromosome\ and g.region.end >= coordStart\ and g.region.start <= coordEnd ] cy = height / 2. geneheight = 1. - 2. * rmargin colBody = [0.75, 0.75, 0.75] colExons = [0.45, 0.45, 0.45] colCDS = [0.1, 0.1, 0.1] colBorder = [0.1, 0.1, 0.1, 1.] colInvisible = [0., 0., 0., 0.] # Legend: Gene body def drawGeneBody(start, end, y, height, rlabel=None, clip_on=True): return drawRoundBox(start=start, end=end, y=y, height=height, fc=colBody + [0.4], ec=colInvisible, rlabel=rlabel, clip_on=clip_on) def drawGeneOutline(start, end, y, height, clip_on=True): drawRoundBox(start=start, end=end, y=y, height=height, fc=colInvisible, ec=colBorder, clip_on=clip_on) drawGeneBody(start=coordEnd + width * 0.01, end=coordEnd + width * 0.01 + 3. * 0.006 * width, y=yB - 0.1 - geneheight, height=geneheight, rlabel='Gene body', clip_on=False) drawGeneOutline(start=coordEnd + width * 0.01, end=coordEnd + width * 0.01 + 3. * 0.006 * width, y=yB - 0.1 - geneheight, height=geneheight, clip_on=False) # Legend: Gene exon def drawGeneExon(start, end, y, height, rlabel=None, clip_on=True): drawBox(start=start, end=end, y=y, height=height, fc=colExons + [1.], ec=colInvisible, rlabel=rlabel, clip_on=clip_on) drawGeneExon(start=coordEnd + width * 0.01, end=coordEnd + width * 0.01 + 3. * 0.006 * width, y=yB - 0.1 - geneheight - 0.1 - geneheight, height=geneheight, rlabel='Exon', clip_on=False) #drawGeneOutline(start = coordEnd + width*0.01, end = coordEnd + width*0.01 + 3. * 0.006 * width, y = yB - 0.1 - geneheight - 0.1 - geneheight, height = geneheight, clip_on = False) # Legend: Gene CDS def drawGeneCDS(start, end, y, height, rlabel=None, clip_on=True): drawBox(start=start, end=end, y=y, height=height, fc=colCDS + [1.], ec=colInvisible, rlabel=rlabel, clip_on=clip_on) drawGeneCDS( start=coordEnd + width * 0.01, end=coordEnd + width * 0.01 + 3. * 0.006 * width, y=yB - 0.1 - geneheight - 0.1 - geneheight - 0.1 - geneheight, height=geneheight, rlabel='CDS', clip_on=False) #drawGeneOutline(start = coordEnd + width*0.01, end = coordEnd + width*0.01 + 3. * 0.006 * width, y = yB - 0.1 - geneheight - 0.1 - geneheight - 0.1 - geneheight, height = geneheight, clip_on = False) # for strand in [False, True]: gh = 0.02 exonh = 0.2 ccy = yA + (cy + 0.5 if strand else cy - 0.5) for g in cgenes: if g.region.strand != strand: continue # Gene body drawGeneBody(start=g.region.start, end=g.region.end, y=ccy - geneheight * .5, height=geneheight) # Exons for r in g.exons: drawGeneExon(start=r.start, end=r.end, y=ccy - geneheight * .5, height=geneheight) # CDS for r in g.CDS: drawGeneCDS(start=r.start, end=r.end, y=ccy - geneheight * .5, height=geneheight) # drawGeneOutline(start=g.region.start, end=g.region.end, y=ccy - geneheight * .5, height=geneheight) for g in cgenes: if g.region.strand == strand: center = (g.region.start + g.region.end) / 2. if center <= coordStart or center >= coordEnd: continue ax.text(center, ccy + (0.65 if strand else -0.65), g.name, horizontalalignment='center', verticalalignment='center') continue # Special handling of curves elif isinstance(rs, curves): ccurve = next((c for c in rs if c.seq == chromosome), None) if ccurve == None: continue # Rasterize - logic required depends on curve type if isinstance(ccurve, fixedStepCurve): iA = max(int((coordStart - ccurve.span) / ccurve.step), 0) iB = max( min(int((coordEnd) / ccurve.step), len(ccurve) - 1), 0) span = ccurve.span step = ccurve.step vals = ccurve[iA:iB + 1] # res = 2048 srcXA = iA * ccurve.step #coordStart srcXB = iB * ccurve.step #coordEnd scale = res / (srcXB - srcXA) ret = [None for _ in range(res)] for i, v in enumerate(vals): dstXA = int( min( max( i * ccurve.step * res / ((iB - iA) * ccurve.step), 0), res - 1)) dstXB = int( min( max( res * (i + ccurve.span / ccurve.step) / (iB - iA), 0), res - 1)) for x in range(dstXA, dstXB + 1): Q = (ccurve.step * (iA + (iB - iA) * float(x) / res), v) if ret[x] is None or Q > ret[x]: ret[x] = Q raster = [v for v in ret if v is not None] elif isinstance(ccurve, variableStepCurve): vals = [ v for v in ccurve if v.start + v.span >= coordStart and v.start <= coordEnd ] # res = 2048 srcXA = coordStart srcXB = coordEnd scale = res / (srcXB - srcXA) ret = [None for _ in range(res)] for v in vals: dstXA = int( min(max((v.start - srcXA) * scale, 0), res - 1)) dstXB = int( min(max((v.start + v.span - srcXA) * scale, 0), res - 1)) for x in range(dstXA, dstXB + 1): ret[x] = (srcXA + (float(x) / scale), v.value) raster = [v for v in ret if v is not None] # Plot curve vBase = 0. vMin = min(y for x, y in raster) vMax = max(y for x, y in raster) yBottom = min(vBase, vMin) yTop = vMax cheight = height predHeight = 1. def V2Y(v): return yA + rmargin + ((v + vBase - yBottom) * vScale) if rs.thresholdValue is not None: yBottom = min(yBottom, rs.thresholdValue) yTop = max(yTop, rs.thresholdValue) cheight -= predHeight vScale = (cheight - rmargin * 2.) / (yTop - yBottom) polypts = [] polypts += [(x, V2Y(max(y, vBase))) for x, y in raster] polypts += reversed([(x, V2Y(min(y, vBase))) for x, y in raster]) poly = Polygon(polypts, fc=color[:3] + [0.4], ec=colInvisible) #ec = [ c*0.5 for c in color[:3] ] + [1.]) ax.add_patch(poly) ax.add_line( Line2D([x for x, y in raster], [V2Y(y) for x, y in raster], linewidth=0.8, color=[c * 0.75 for c in color[:3]] + [0.75])) #color = color)) # Legend lyA = V2Y(vMin) lyB = V2Y(vMax) ax.add_patch( Rectangle((coordEnd, lyA), 1. * 0.006 * width, lyB - lyA, fc=(0.2, 0.2, 0.2, 1.), ec=colInvisible, clip_on=False)) tticks = [(vMax, str(vMax)), (vMin, str(vMin))] if rs.thresholdValue is not None: tticks.append( (rs.thresholdValue, str(rs.thresholdValue) + ' (threshold)')) if vMin < 0. and vMax > 0.: tticks.append((0.0, str(0.0))) tticks = sorted(tticks, key=lambda p: p[0]) for v, n in tticks: ly = V2Y(v) ax.add_line( Line2D([ coordEnd + 0. * 0.006 * width, coordEnd + 2. * 0.006 * width ], [ly, ly], color=(0.2, 0.2, 0.2, 1.), linewidth=1., clip_on=False)) plt.text(coordEnd + 3. * 0.006 * width, ly, n, verticalalignment='center', horizontalalignment='left', color=ax.yaxis.label.get_color(), fontsize=8, clip_on=False) # Thresholded if rs.thresholdValue is not None: tthr = V2Y(rs.thresholdValue) ax.plot([coordStart, coordEnd], [tthr, tthr], linestyle='-', color='grey', label='Expected at random') frs = rs.regions().filter('', lambda r: r.seq == chromosome\ and r.end >= coordStart\ and r.start <= coordEnd) for r in frs: drawRoundBox(start=r.start, end=r.end, y=yB - predHeight + rmargin, height=predHeight - 2 * rmargin, fc=color[:3] + [0.4], ec=[c * 0.5 for c in color[:3]] + [1.]) # Legend drawRoundBox(start=coordEnd + width * 0.01, end=coordEnd + width * 0.01 + 3. * 0.006 * width, y=yB - 0.1 - geneheight, height=geneheight, fc=color[:3] + [0.4], ec=[c * 0.5 for c in color[:3]] + [1.], rlabel='Thresholded', clip_on=False) continue # Simpler handling of region sets frs = rs.filter('', lambda r: r.seq == chromosome\ and r.end >= coordStart\ and r.start <= coordEnd) for r in frs: drawRoundBox(start=r.start, end=r.end, y=yA + rmargin, height=height - 2 * rmargin, fc=color[:3] + [0.4], ec=[c * 0.5 for c in color[:3]] + [1.]) # plt.xlabel('Chromosome ' + chromosome, fontsize=18) fig.tight_layout() if outpath is None: bio = BytesIO() fig.savefig(bio, format='png') plt.close('all') encoded = base64.b64encode(bio.getvalue()).decode('utf-8') html = '<img src=\'data:image/png;base64,%s\'>' % encoded display(HTML(html)) else: fig.savefig(outpath) plt.close('all') # except ImportError as err: raise err
def plotInclusiveResponse(dataframe, name): fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(3, 6), constrained_layout=True) fig.suptitle("Inclusive response") trans1 = ax.transData + ScaledTranslation(-5 / 72, 0, fig.dpi_scale_trans) trans2 = ax.transData + ScaledTranslation(+5 / 72, 0, fig.dpi_scale_trans) colors = [sns.xkcd_rgb["cerulean"], sns.xkcd_rgb["rouge"]] gluons_l1l2l3 = dataframe.loc[dataframe.isPhysG == 1, "jetPt"] / dataframe.loc[dataframe.isPhysG == 1, "genJetPt"] uds_l1l2l3 = dataframe.loc[dataframe.isPhysG != 1, "jetPt"] / dataframe.loc[dataframe.isPhysG != 1, "genJetPt"] gluons_dnn = dataframe.loc[dataframe.isPhysG == 1, "response"] uds_dnn = dataframe.loc[dataframe.isPhysG != 1, "response"] mean_g_l1l2l3, std_g_l1l2l3 = norm.fit(gluons_l1l2l3) mean_uds_l1l2l3, std_uds_l1l2l3 = norm.fit(uds_l1l2l3) median_g_l1l2l3 = np.median(gluons_l1l2l3) median_uds_l1l2l3 = np.median(uds_l1l2l3) mean_g_dnn, std_g_dnn = norm.fit(gluons_dnn) mean_uds_dnn, std_uds_dnn = norm.fit(uds_dnn) median_g_dnn = np.median(gluons_dnn) median_uds_dnn = np.median(uds_dnn) iqr_g_l1l2l3 = np.subtract(*np.percentile(gluons_l1l2l3, [75, 25])) * 0.1 iqr_uds_l1l2l3 = np.subtract(*np.percentile(uds_l1l2l3, [75, 25])) * 0.1 iqr_g_dnn = np.subtract(*np.percentile(gluons_dnn, [75, 25])) * 0.1 iqr_uds_dnn = np.subtract(*np.percentile(uds_dnn, [75, 25])) * 0.1 ax.set_xlim(-1.0, 2.0) ax.set_ylim(0.975, 1.04) ax.text(-0.5, 1.041, "60 GeV < p$_T$ < 600 GeV, |$\eta$| < 2.5", fontsize=7) ax.set_ylabel("Median response") ax.set_xlabel("Jet class") plt.errorbar(x=['g', 'uds'], y=[median_g_l1l2l3, median_uds_l1l2l3], fmt='o', label="L1L2L3", color=colors[1], ls='none', ecolor='k', transform=trans1) plt.errorbar(x=['g', 'uds'], y=[median_g_dnn, median_uds_dnn], yerr=[iqr_g_dnn, iqr_uds_dnn], fmt='o', label="DNN", color=colors[0], ls='none', ecolor='k', transform=trans2) ax.plot([-1.0, 2.0], [1.0, 1.0], linestyle='--', linewidth=1.5, color='k') ax.legend() plt.savefig("./plots/InclusiveResponse.pdf".format(name)) plt.clf() plt.close()