Module snowpat.SnowLense.plot_snowpack
Expand source code
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.collections import QuadMesh
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
import matplotlib as mpl
import pandas as pd
import datetime
import numpy as np
from typing import Optional, List
from .plot_helpers_snowpack import PlotHelper, CustomFormatter, _add_crust_lines
from .plotting import plotProfile
from snowpat.snowpackreader import SnowpackReader
from .Utils import show_figure
class SnowpackPlotter:
def __init__(self, snowpack: SnowpackReader, savefile=None):
"""
Initializes an instance of the SnowpackPlotter class.
Args:
snowpack (SnowpackReader): The snowpack reader object.
savefile (str, optional): The path to save the plot. Defaults to None.
"""
if not isinstance(snowpack, SnowpackReader):
raise ValueError(f"A SnowpackReader object has to be provided, got {type(snowpack).__name__}")
self.savefile = savefile
self.snowpack = snowpack
self.latest_fig: Optional[plt.Figure] = None
self.plot_helper = PlotHelper()
# internal variables
self.n_cols = 1
def plot(self, var_codes:List[str] = ["0513"],**kwargs) -> plt.Figure:
"""
Plots the data for the given variable codes.
A figure is created with subplots for each variable code.
Args:
var_codes (list): A list of variable codes to plot.
**kwargs: Arbitrary keyword arguments for additional configuration.
Returns:
matplotlib.pyplot.Figure: The created figure.
Optional keyword arguments:
- start (datetime): The start date for the plot.
- stop (datetime): The stop date for the plot.
- resolution (str): The resolution for the plot.
- num_ticks (int): The number of ticks on the x-axis.
- cmap (Union[mcolors.Colormap, Dict[str, mcolors.Colormap]]): The colormap for the plot.
- norm (Union[mcolors.Normalize, Dict[str, mcolors.Normalize]]): The normalization for the plot.
- cbar_label (Union[str, Dict[str,str]]): The colorbar label for the plot.
- n_cols (int): The number of columns for the subplots.
- title (str): The title for the plot.
- vmin (Union[int, Dict[str,int]]): The minimum value for the colormap.
- vmax (Union[int, Dict[str,int]]): The maximum value for the colormap.
- adjust_data (Callable[[str,np.ndarray],np.ndarray]): A function for adjusting the data.
- single_ticks (bool): Whether to use a single x-axis/y-axis for all subplots. (default: True)
- set_ylabel (bool): Whether to set the y-axis label. (default: True)
- colorbar (bool): Whether to display the colorbar. (default: True)
- ind_mfcrust (bool): Whether to indicate the MFCrust specifically. (default: True)
- mfcrust_color (bool): Plot the meltcrust in orange instead of read. (default: False)
- subtitle (Dict[str,str]): A dictionary with subtitles for each variable code.
All other keyword arguments are passed to the pcolormesh function.
Raises:
ValueError: If any of the variable codes are not valid.
RuntimeError: If there is a problem during plotting.
"""
dates = self._init_data(var_codes)
kwargs = self._parse_kwargs(**kwargs)
plot_dates, tick_dates = self._aggregateData(var_codes, dates)
# calculate the number of rows and cols for the subplots
n_subplots = len(var_codes)
n_rows = n_subplots // self.plot_helper.n_cols
if n_subplots % self.plot_helper.n_cols != 0:
n_rows += 1
# adjust the figure size so it stays nicely looking within a grid
fig_x = 6.4 * (1+ 2.0 * (self.plot_helper.n_cols-1)) if self.plot_helper.n_cols > 1 else 6.4
fig_y = 4.8 * (1 + 1.0* (n_rows - 1)) if n_rows > 1 and self.plot_helper.n_cols > 1 else 4.8 * (1 + 0.2* (n_rows - 1))
fig, axes = plt.subplots(n_rows, self.plot_helper.n_cols, figsize=(fig_x,fig_y), sharex=self.plot_helper.single_ticks, sharey=self.plot_helper.single_ticks)
# add some space between the subplots, to make space for ticks and cbars
wspace = 0.4 if self.plot_helper.n_cols > 1 and not self.plot_helper.single_ticks else 0.3
hspace = 0.4 if n_rows > 1 else 0.2
fig.subplots_adjust(hspace=hspace, wspace=wspace)
if self.plot_helper.title:
fig.suptitle(self.plot_helper.title, fontsize=16)
# set the indexes at which subplot the x/y labels should be set
if self.plot_helper.n_cols > 1 and n_rows > 1:
ids_ylabels = [i for i in range(1, n_subplots+1) if i % self.plot_helper.n_cols == 1]
ids_xlabels = [i for i in range(n_subplots-self.plot_helper.n_cols+1, n_subplots+1)]
elif self.plot_helper.n_cols == 1 and n_rows > 1:
ids_ylabels = [i for i in range(1, n_subplots+1)]
ids_xlabels = [n_subplots]
elif self.plot_helper.n_cols > 1 and n_rows == 1:
ids_ylabels = [1]
ids_xlabels = [i for i in range(1, n_subplots+1)]
else:
ids_xlabels = [1]
ids_ylabels = [1]
row = 0
col = 0
for sub_id in range(1, n_subplots+1):
var_code = var_codes[sub_id-1]
# if it is only one subplot, axes is not an array
if isinstance(axes, mpl.axes.Axes):
ax = axes
else:
# we need to get the correct axes object, whether it is a 1D or 2D array
ax = axes[row, col] if self.plot_helper.n_cols > 1 and n_rows > 1 else axes[row]
row += 1 if (sub_id % self.plot_helper.n_cols == 1) or (self.plot_helper.n_cols == 1 or n_rows == 1) else 0
col = (col+1) % self.plot_helper.n_cols
cmap = self.plot_helper.getCmap(var_code)
norm = self.plot_helper.getNorm(var_code)
cmap.set_bad(ax.get_facecolor())
mpl.rcParams['hatch.linewidth'] = 3.0
plot_dates_len = len(plot_dates)
for date_id, plot_date in enumerate(plot_dates):
# set the bar to the correct date, i.e. the x boundaries
x = [plot_date, plot_dates[date_id+1]] if date_id < plot_dates_len-1 else [plot_date, plot_date + pd.Timedelta(days=1)]
profile = self.snowpack.get_profile_on(plot_date)
# get the layer boundaries, which will be the y boundaries
h = profile.get_param("0501") # "0501" are the layer boundaries
# the data in the cells
data = profile.get_param(var_code, return_missing=True)
data = self.plot_helper.handleData(var_code, data)
if h.size <= 1: continue
# we need to handle missing values
mesh = np.array([data,])
conditions = mesh== -999
masked_mesh = np.ma.masked_where(conditions, mesh)
cplot = ax.pcolormesh(x, h, masked_mesh.transpose(), norm=norm, cmap=cmap, **kwargs)
if var_code == "0513":
# this function will add the crust lines to the plot, if it any is set to true
_add_crust_lines(ax, x, h, mesh, self.plot_helper.ind_mfcrust, self.plot_helper.mfcrust_color)
# set the x and y limits, for better visualization
ax.set_xlim(self.plot_helper.start, self.plot_helper.stop)
hmax = self.snowpack.get_profile_on(self.plot_helper.stop).get_param("0501")[-1]
ymax = hmax if hmax > 250 else 250
ymin,_ = ax.get_ylim()
ax.set_ylim(ymin, ymax)
self._setColorbar(ax, cplot, var_code, fig)
self._set_title(ax, var_code)
# set xticks
if (self.plot_helper.single_ticks and sub_id in ids_xlabels) or not self.plot_helper.single_ticks:
if self.plot_helper.num_ticks:
ax.set_xticks(tick_dates)
labels = [mdates.num2date(label).strftime('%Y-%m-%d') for label in ax.get_xticks()]
ax.set_xticklabels(labels, rotation=30)
else:
custom_formatter = CustomFormatter('%m-%d', '%m-%d\n%Y')
ax.xaxis.set_major_formatter(ticker.FuncFormatter(custom_formatter))
ax.set_xlabel("Date")
if (self.plot_helper.single_ticks and sub_id in ids_ylabels) or not self.plot_helper.single_ticks and self.plot_helper.set_ylabel:
ax.set_ylabel("HS [cm]")
self.latest_fig = fig
plt.close(fig)
return self.latest_fig
def save(self, outfile: str = None):
"""
Save the latest figure to an output file.
Args:
outfile (str, optional): The path to the output file. If not provided,
the figure will be saved to the default savefile specified during
object initialization.
Raises:
ValueError: If no output file is provided.
"""
if outfile:
self.latest_fig.savefig(outfile)
elif self.savefile:
self.latest_fig.savefig(self.savefile)
else:
raise ValueError("No output file provided")
def getSubplot(self, index: int) -> plt.Axes:
"""
Retrieve a subplot from the latest figure.
Parameters:
index (int): The index of the subplot to retrieve.
Returns:
plt.Axes: The requested subplot.
"""
n_cols = self.plot_helper.n_cols
row = (index-1) // n_cols if n_cols > 1 else index
col = (index-1) % n_cols if n_cols > 1 else 0
return self.latest_fig.get_axes()[row] if n_cols == 1 else self.latest_fig.get_axes()[row, col]
def getLatestFigure(self)->plt.Figure:
return self.latest_fig
def plotProfileOn(self, date:datetime, **kwargs):
profile = self.snowpack.get_profile_on(date)
self.latest_fig = plotProfile(profile, **kwargs)
return self.latest_fig
def show(self):
"""
Displays the latest figure of the snowpack.
"""
show_figure(self.latest_fig)
def _parse_kwargs(self, **kwargs):
if "start" in kwargs:
self.plot_helper.start = kwargs.pop("start")
if "stop" in kwargs:
self.plot_helper.stop = kwargs.pop("stop")
if "resolution" in kwargs:
self.plot_helper.resolution = kwargs.pop("resolution")
if "cbar_label" in kwargs:
cbar_label = kwargs.pop("cbar_label")
if cbar_label is None:
self.plot_helper.set_cbar_label = False
else:
self.plot_helper.cbar_label = cbar_label
if "n_cols" in kwargs:
self.plot_helper.n_cols = kwargs.pop("n_cols")
if "num_ticks" in kwargs:
self.plot_helper.num_ticks = kwargs.pop("num_ticks")
if "cmap" in kwargs:
cmap = kwargs.pop("cmap")
if isinstance(cmap, mcolors.Colormap):
self.plot_helper.cmap = cmap
elif isinstance(cmap, dict):
self.plot_helper.cmap_dict = cmap
else:
raise ValueError("cmap must be a mcolors.Colormap object or a dict with var_codes as keys and mcolors.Colormap objects as values")
if "norm" in kwargs:
norm = kwargs.pop("norm")
if isinstance(norm, mcolors.Normalize):
self.plot_helper.norm = norm
elif isinstance(norm, dict):
self.plot_helper.norm_dict = norm
else:
raise ValueError("norm must be a mcolors.Normalize object or a dict with var_codes as keys and mcolors.Normalize objects as values")
if "title" in kwargs:
self.plot_helper.title = kwargs.pop("title")
if "vmin" in kwargs and "vmax" in kwargs:
vmin = kwargs.pop("vmin")
vmax = kwargs.pop("vmax")
if isinstance(vmin, int) and isinstance(vmax, int):
self.plot_helper.norm = mcolors.Normalize(vmin=vmin, vmax=vmax)
elif isinstance(vmin, dict) and isinstance(vmax, dict):
self.plot_helper.norm_dict = {var_code: mcolors.Normalize(vmin=vmin[var_code], vmax=vmax[var_code]) for var_code in vmin.keys()}
elif ("vmin" in kwargs and not "vmax" in kwargs) or (not "vmin" in kwargs and "vmax" in kwargs):
raise ValueError("Both vmin and vmax must be provided")
if "adjust_data" in kwargs:
self.plot_helper.adjust_data = kwargs.pop("adjust_data")
if "subtitle" in kwargs:
self.plot_helper.subtitle = kwargs.pop("subtitle")
self.plot_helper.single_ticks = kwargs.pop("single_ticks", True)
self.plot_helper.set_ylabel = kwargs.pop("set_ylabel", True)
self.plot_helper.colorbar = kwargs.pop("colorbar", True)
self.plot_helper.ind_mfcrust = kwargs.pop("ind_mfcrust", True)
self.plot_helper.mfcrust_color = kwargs.pop("mfcrust_color", False)
return kwargs
def _init_data(self, var_codes):
for var_code in var_codes:
if not self.snowpack.DataCodes.get(var_code):
raise ValueError(f"Invalid variable code: {var_code}")
dates = self.snowpack.get_all_dates()
self.plot_helper.start = dates[0]
self.plot_helper.stop = dates[-1]
return dates
def _aggregateData(self, var_codes, dates):
start = self.plot_helper.start
stop = self.plot_helper.stop
if self.plot_helper.resolution:
dates = pd.date_range(start, stop, freq=self.plot_helper.resolution)
plot_dates = []
for date in dates:
profile = self.snowpack.get_profile_on(date)
if profile:
plot_dates.append(date)
tick_dates = None
if self.plot_helper.num_ticks:
tick_dates = pd.date_range(start, stop, freq=(stop-start)/self.plot_helper.num_ticks)
if (stop-start) > pd.Timedelta(days=1):
tick_dates = tick_dates.floor("D")
else:
tick_dates = tick_dates.floor("H")
return plot_dates, tick_dates
def _setColorbar(self, ax:plt.Axes, cplot:QuadMesh, var_code:str, fig:plt.Figure):
if self.plot_helper.colorbar:
ax_pos = ax.get_position()
cax = fig.add_axes([ax_pos.x1+0.01, ax_pos.y0, 0.02, ax_pos.height])
cbar = plt.colorbar(cplot, cax=cax)
label, is_available = self.plot_helper.getCbarLabel(var_code)
if is_available:
cbar.set_label(label, fontsize=10)
else:
name = self.snowpack.DataCodes.get(var_code, var_code)
unit = self.snowpack.units.get(var_code, "b.E.")
label = name + "\n[" + unit + "]"
cbar.set_label(label, fontsize=10)
if var_code == "0513":
cbar.set_ticks([1,2,3,4,5,6,7,8,9])
cbar.set_ticklabels(['PP', 'DF', 'RG', 'FC', 'DH', 'SH', 'MF', 'IF', 'FCxr'])
cbar.minorticks_off()
def _set_title(self, ax:plt.Axes, var_code:str):
title, is_available = self.plot_helper.getSubtitle(var_code)
if is_available:
ax.set_title(title)
Classes
class SnowpackPlotter (snowpack: SnowpackReader, savefile=None)-
Initializes an instance of the SnowpackPlotter class.
Args
snowpack:SnowpackReader- The snowpack reader object.
savefile:str, optional- The path to save the plot. Defaults to None.
Expand source code
class SnowpackPlotter: def __init__(self, snowpack: SnowpackReader, savefile=None): """ Initializes an instance of the SnowpackPlotter class. Args: snowpack (SnowpackReader): The snowpack reader object. savefile (str, optional): The path to save the plot. Defaults to None. """ if not isinstance(snowpack, SnowpackReader): raise ValueError(f"A SnowpackReader object has to be provided, got {type(snowpack).__name__}") self.savefile = savefile self.snowpack = snowpack self.latest_fig: Optional[plt.Figure] = None self.plot_helper = PlotHelper() # internal variables self.n_cols = 1 def plot(self, var_codes:List[str] = ["0513"],**kwargs) -> plt.Figure: """ Plots the data for the given variable codes. A figure is created with subplots for each variable code. Args: var_codes (list): A list of variable codes to plot. **kwargs: Arbitrary keyword arguments for additional configuration. Returns: matplotlib.pyplot.Figure: The created figure. Optional keyword arguments: - start (datetime): The start date for the plot. - stop (datetime): The stop date for the plot. - resolution (str): The resolution for the plot. - num_ticks (int): The number of ticks on the x-axis. - cmap (Union[mcolors.Colormap, Dict[str, mcolors.Colormap]]): The colormap for the plot. - norm (Union[mcolors.Normalize, Dict[str, mcolors.Normalize]]): The normalization for the plot. - cbar_label (Union[str, Dict[str,str]]): The colorbar label for the plot. - n_cols (int): The number of columns for the subplots. - title (str): The title for the plot. - vmin (Union[int, Dict[str,int]]): The minimum value for the colormap. - vmax (Union[int, Dict[str,int]]): The maximum value for the colormap. - adjust_data (Callable[[str,np.ndarray],np.ndarray]): A function for adjusting the data. - single_ticks (bool): Whether to use a single x-axis/y-axis for all subplots. (default: True) - set_ylabel (bool): Whether to set the y-axis label. (default: True) - colorbar (bool): Whether to display the colorbar. (default: True) - ind_mfcrust (bool): Whether to indicate the MFCrust specifically. (default: True) - mfcrust_color (bool): Plot the meltcrust in orange instead of read. (default: False) - subtitle (Dict[str,str]): A dictionary with subtitles for each variable code. All other keyword arguments are passed to the pcolormesh function. Raises: ValueError: If any of the variable codes are not valid. RuntimeError: If there is a problem during plotting. """ dates = self._init_data(var_codes) kwargs = self._parse_kwargs(**kwargs) plot_dates, tick_dates = self._aggregateData(var_codes, dates) # calculate the number of rows and cols for the subplots n_subplots = len(var_codes) n_rows = n_subplots // self.plot_helper.n_cols if n_subplots % self.plot_helper.n_cols != 0: n_rows += 1 # adjust the figure size so it stays nicely looking within a grid fig_x = 6.4 * (1+ 2.0 * (self.plot_helper.n_cols-1)) if self.plot_helper.n_cols > 1 else 6.4 fig_y = 4.8 * (1 + 1.0* (n_rows - 1)) if n_rows > 1 and self.plot_helper.n_cols > 1 else 4.8 * (1 + 0.2* (n_rows - 1)) fig, axes = plt.subplots(n_rows, self.plot_helper.n_cols, figsize=(fig_x,fig_y), sharex=self.plot_helper.single_ticks, sharey=self.plot_helper.single_ticks) # add some space between the subplots, to make space for ticks and cbars wspace = 0.4 if self.plot_helper.n_cols > 1 and not self.plot_helper.single_ticks else 0.3 hspace = 0.4 if n_rows > 1 else 0.2 fig.subplots_adjust(hspace=hspace, wspace=wspace) if self.plot_helper.title: fig.suptitle(self.plot_helper.title, fontsize=16) # set the indexes at which subplot the x/y labels should be set if self.plot_helper.n_cols > 1 and n_rows > 1: ids_ylabels = [i for i in range(1, n_subplots+1) if i % self.plot_helper.n_cols == 1] ids_xlabels = [i for i in range(n_subplots-self.plot_helper.n_cols+1, n_subplots+1)] elif self.plot_helper.n_cols == 1 and n_rows > 1: ids_ylabels = [i for i in range(1, n_subplots+1)] ids_xlabels = [n_subplots] elif self.plot_helper.n_cols > 1 and n_rows == 1: ids_ylabels = [1] ids_xlabels = [i for i in range(1, n_subplots+1)] else: ids_xlabels = [1] ids_ylabels = [1] row = 0 col = 0 for sub_id in range(1, n_subplots+1): var_code = var_codes[sub_id-1] # if it is only one subplot, axes is not an array if isinstance(axes, mpl.axes.Axes): ax = axes else: # we need to get the correct axes object, whether it is a 1D or 2D array ax = axes[row, col] if self.plot_helper.n_cols > 1 and n_rows > 1 else axes[row] row += 1 if (sub_id % self.plot_helper.n_cols == 1) or (self.plot_helper.n_cols == 1 or n_rows == 1) else 0 col = (col+1) % self.plot_helper.n_cols cmap = self.plot_helper.getCmap(var_code) norm = self.plot_helper.getNorm(var_code) cmap.set_bad(ax.get_facecolor()) mpl.rcParams['hatch.linewidth'] = 3.0 plot_dates_len = len(plot_dates) for date_id, plot_date in enumerate(plot_dates): # set the bar to the correct date, i.e. the x boundaries x = [plot_date, plot_dates[date_id+1]] if date_id < plot_dates_len-1 else [plot_date, plot_date + pd.Timedelta(days=1)] profile = self.snowpack.get_profile_on(plot_date) # get the layer boundaries, which will be the y boundaries h = profile.get_param("0501") # "0501" are the layer boundaries # the data in the cells data = profile.get_param(var_code, return_missing=True) data = self.plot_helper.handleData(var_code, data) if h.size <= 1: continue # we need to handle missing values mesh = np.array([data,]) conditions = mesh== -999 masked_mesh = np.ma.masked_where(conditions, mesh) cplot = ax.pcolormesh(x, h, masked_mesh.transpose(), norm=norm, cmap=cmap, **kwargs) if var_code == "0513": # this function will add the crust lines to the plot, if it any is set to true _add_crust_lines(ax, x, h, mesh, self.plot_helper.ind_mfcrust, self.plot_helper.mfcrust_color) # set the x and y limits, for better visualization ax.set_xlim(self.plot_helper.start, self.plot_helper.stop) hmax = self.snowpack.get_profile_on(self.plot_helper.stop).get_param("0501")[-1] ymax = hmax if hmax > 250 else 250 ymin,_ = ax.get_ylim() ax.set_ylim(ymin, ymax) self._setColorbar(ax, cplot, var_code, fig) self._set_title(ax, var_code) # set xticks if (self.plot_helper.single_ticks and sub_id in ids_xlabels) or not self.plot_helper.single_ticks: if self.plot_helper.num_ticks: ax.set_xticks(tick_dates) labels = [mdates.num2date(label).strftime('%Y-%m-%d') for label in ax.get_xticks()] ax.set_xticklabels(labels, rotation=30) else: custom_formatter = CustomFormatter('%m-%d', '%m-%d\n%Y') ax.xaxis.set_major_formatter(ticker.FuncFormatter(custom_formatter)) ax.set_xlabel("Date") if (self.plot_helper.single_ticks and sub_id in ids_ylabels) or not self.plot_helper.single_ticks and self.plot_helper.set_ylabel: ax.set_ylabel("HS [cm]") self.latest_fig = fig plt.close(fig) return self.latest_fig def save(self, outfile: str = None): """ Save the latest figure to an output file. Args: outfile (str, optional): The path to the output file. If not provided, the figure will be saved to the default savefile specified during object initialization. Raises: ValueError: If no output file is provided. """ if outfile: self.latest_fig.savefig(outfile) elif self.savefile: self.latest_fig.savefig(self.savefile) else: raise ValueError("No output file provided") def getSubplot(self, index: int) -> plt.Axes: """ Retrieve a subplot from the latest figure. Parameters: index (int): The index of the subplot to retrieve. Returns: plt.Axes: The requested subplot. """ n_cols = self.plot_helper.n_cols row = (index-1) // n_cols if n_cols > 1 else index col = (index-1) % n_cols if n_cols > 1 else 0 return self.latest_fig.get_axes()[row] if n_cols == 1 else self.latest_fig.get_axes()[row, col] def getLatestFigure(self)->plt.Figure: return self.latest_fig def plotProfileOn(self, date:datetime, **kwargs): profile = self.snowpack.get_profile_on(date) self.latest_fig = plotProfile(profile, **kwargs) return self.latest_fig def show(self): """ Displays the latest figure of the snowpack. """ show_figure(self.latest_fig) def _parse_kwargs(self, **kwargs): if "start" in kwargs: self.plot_helper.start = kwargs.pop("start") if "stop" in kwargs: self.plot_helper.stop = kwargs.pop("stop") if "resolution" in kwargs: self.plot_helper.resolution = kwargs.pop("resolution") if "cbar_label" in kwargs: cbar_label = kwargs.pop("cbar_label") if cbar_label is None: self.plot_helper.set_cbar_label = False else: self.plot_helper.cbar_label = cbar_label if "n_cols" in kwargs: self.plot_helper.n_cols = kwargs.pop("n_cols") if "num_ticks" in kwargs: self.plot_helper.num_ticks = kwargs.pop("num_ticks") if "cmap" in kwargs: cmap = kwargs.pop("cmap") if isinstance(cmap, mcolors.Colormap): self.plot_helper.cmap = cmap elif isinstance(cmap, dict): self.plot_helper.cmap_dict = cmap else: raise ValueError("cmap must be a mcolors.Colormap object or a dict with var_codes as keys and mcolors.Colormap objects as values") if "norm" in kwargs: norm = kwargs.pop("norm") if isinstance(norm, mcolors.Normalize): self.plot_helper.norm = norm elif isinstance(norm, dict): self.plot_helper.norm_dict = norm else: raise ValueError("norm must be a mcolors.Normalize object or a dict with var_codes as keys and mcolors.Normalize objects as values") if "title" in kwargs: self.plot_helper.title = kwargs.pop("title") if "vmin" in kwargs and "vmax" in kwargs: vmin = kwargs.pop("vmin") vmax = kwargs.pop("vmax") if isinstance(vmin, int) and isinstance(vmax, int): self.plot_helper.norm = mcolors.Normalize(vmin=vmin, vmax=vmax) elif isinstance(vmin, dict) and isinstance(vmax, dict): self.plot_helper.norm_dict = {var_code: mcolors.Normalize(vmin=vmin[var_code], vmax=vmax[var_code]) for var_code in vmin.keys()} elif ("vmin" in kwargs and not "vmax" in kwargs) or (not "vmin" in kwargs and "vmax" in kwargs): raise ValueError("Both vmin and vmax must be provided") if "adjust_data" in kwargs: self.plot_helper.adjust_data = kwargs.pop("adjust_data") if "subtitle" in kwargs: self.plot_helper.subtitle = kwargs.pop("subtitle") self.plot_helper.single_ticks = kwargs.pop("single_ticks", True) self.plot_helper.set_ylabel = kwargs.pop("set_ylabel", True) self.plot_helper.colorbar = kwargs.pop("colorbar", True) self.plot_helper.ind_mfcrust = kwargs.pop("ind_mfcrust", True) self.plot_helper.mfcrust_color = kwargs.pop("mfcrust_color", False) return kwargs def _init_data(self, var_codes): for var_code in var_codes: if not self.snowpack.DataCodes.get(var_code): raise ValueError(f"Invalid variable code: {var_code}") dates = self.snowpack.get_all_dates() self.plot_helper.start = dates[0] self.plot_helper.stop = dates[-1] return dates def _aggregateData(self, var_codes, dates): start = self.plot_helper.start stop = self.plot_helper.stop if self.plot_helper.resolution: dates = pd.date_range(start, stop, freq=self.plot_helper.resolution) plot_dates = [] for date in dates: profile = self.snowpack.get_profile_on(date) if profile: plot_dates.append(date) tick_dates = None if self.plot_helper.num_ticks: tick_dates = pd.date_range(start, stop, freq=(stop-start)/self.plot_helper.num_ticks) if (stop-start) > pd.Timedelta(days=1): tick_dates = tick_dates.floor("D") else: tick_dates = tick_dates.floor("H") return plot_dates, tick_dates def _setColorbar(self, ax:plt.Axes, cplot:QuadMesh, var_code:str, fig:plt.Figure): if self.plot_helper.colorbar: ax_pos = ax.get_position() cax = fig.add_axes([ax_pos.x1+0.01, ax_pos.y0, 0.02, ax_pos.height]) cbar = plt.colorbar(cplot, cax=cax) label, is_available = self.plot_helper.getCbarLabel(var_code) if is_available: cbar.set_label(label, fontsize=10) else: name = self.snowpack.DataCodes.get(var_code, var_code) unit = self.snowpack.units.get(var_code, "b.E.") label = name + "\n[" + unit + "]" cbar.set_label(label, fontsize=10) if var_code == "0513": cbar.set_ticks([1,2,3,4,5,6,7,8,9]) cbar.set_ticklabels(['PP', 'DF', 'RG', 'FC', 'DH', 'SH', 'MF', 'IF', 'FCxr']) cbar.minorticks_off() def _set_title(self, ax:plt.Axes, var_code:str): title, is_available = self.plot_helper.getSubtitle(var_code) if is_available: ax.set_title(title)Methods
def getLatestFigure(self) ‑> matplotlib.figure.Figure-
Expand source code
def getLatestFigure(self)->plt.Figure: return self.latest_fig def getSubplot(self, index: int) ‑> matplotlib.axes._axes.Axes-
Retrieve a subplot from the latest figure.
Parameters
index (int): The index of the subplot to retrieve.
Returns
plt.Axes- The requested subplot.
Expand source code
def getSubplot(self, index: int) -> plt.Axes: """ Retrieve a subplot from the latest figure. Parameters: index (int): The index of the subplot to retrieve. Returns: plt.Axes: The requested subplot. """ n_cols = self.plot_helper.n_cols row = (index-1) // n_cols if n_cols > 1 else index col = (index-1) % n_cols if n_cols > 1 else 0 return self.latest_fig.get_axes()[row] if n_cols == 1 else self.latest_fig.get_axes()[row, col] def plot(self, var_codes: List[str] = ['0513'], **kwargs) ‑> matplotlib.figure.Figure-
Plots the data for the given variable codes.
A figure is created with subplots for each variable code.
Args
var_codes:list- A list of variable codes to plot.
**kwargs- Arbitrary keyword arguments for additional configuration.
Returns
matplotlib.pyplot.Figure- The created figure.
Optional keyword arguments: - start (datetime): The start date for the plot. - stop (datetime): The stop date for the plot. - resolution (str): The resolution for the plot. - num_ticks (int): The number of ticks on the x-axis. - cmap (Union[mcolors.Colormap, Dict[str, mcolors.Colormap]]): The colormap for the plot. - norm (Union[mcolors.Normalize, Dict[str, mcolors.Normalize]]): The normalization for the plot. - cbar_label (Union[str, Dict[str,str]]): The colorbar label for the plot. - n_cols (int): The number of columns for the subplots. - title (str): The title for the plot. - vmin (Union[int, Dict[str,int]]): The minimum value for the colormap. - vmax (Union[int, Dict[str,int]]): The maximum value for the colormap. - adjust_data (Callable[[str,np.ndarray],np.ndarray]): A function for adjusting the data. - single_ticks (bool): Whether to use a single x-axis/y-axis for all subplots. (default: True) - set_ylabel (bool): Whether to set the y-axis label. (default: True) - colorbar (bool): Whether to display the colorbar. (default: True) - ind_mfcrust (bool): Whether to indicate the MFCrust specifically. (default: True) - mfcrust_color (bool): Plot the meltcrust in orange instead of read. (default: False) - subtitle (Dict[str,str]): A dictionary with subtitles for each variable code.
All other keyword arguments are passed to the pcolormesh function.
Raises
ValueError- If any of the variable codes are not valid.
RuntimeError- If there is a problem during plotting.
Expand source code
def plot(self, var_codes:List[str] = ["0513"],**kwargs) -> plt.Figure: """ Plots the data for the given variable codes. A figure is created with subplots for each variable code. Args: var_codes (list): A list of variable codes to plot. **kwargs: Arbitrary keyword arguments for additional configuration. Returns: matplotlib.pyplot.Figure: The created figure. Optional keyword arguments: - start (datetime): The start date for the plot. - stop (datetime): The stop date for the plot. - resolution (str): The resolution for the plot. - num_ticks (int): The number of ticks on the x-axis. - cmap (Union[mcolors.Colormap, Dict[str, mcolors.Colormap]]): The colormap for the plot. - norm (Union[mcolors.Normalize, Dict[str, mcolors.Normalize]]): The normalization for the plot. - cbar_label (Union[str, Dict[str,str]]): The colorbar label for the plot. - n_cols (int): The number of columns for the subplots. - title (str): The title for the plot. - vmin (Union[int, Dict[str,int]]): The minimum value for the colormap. - vmax (Union[int, Dict[str,int]]): The maximum value for the colormap. - adjust_data (Callable[[str,np.ndarray],np.ndarray]): A function for adjusting the data. - single_ticks (bool): Whether to use a single x-axis/y-axis for all subplots. (default: True) - set_ylabel (bool): Whether to set the y-axis label. (default: True) - colorbar (bool): Whether to display the colorbar. (default: True) - ind_mfcrust (bool): Whether to indicate the MFCrust specifically. (default: True) - mfcrust_color (bool): Plot the meltcrust in orange instead of read. (default: False) - subtitle (Dict[str,str]): A dictionary with subtitles for each variable code. All other keyword arguments are passed to the pcolormesh function. Raises: ValueError: If any of the variable codes are not valid. RuntimeError: If there is a problem during plotting. """ dates = self._init_data(var_codes) kwargs = self._parse_kwargs(**kwargs) plot_dates, tick_dates = self._aggregateData(var_codes, dates) # calculate the number of rows and cols for the subplots n_subplots = len(var_codes) n_rows = n_subplots // self.plot_helper.n_cols if n_subplots % self.plot_helper.n_cols != 0: n_rows += 1 # adjust the figure size so it stays nicely looking within a grid fig_x = 6.4 * (1+ 2.0 * (self.plot_helper.n_cols-1)) if self.plot_helper.n_cols > 1 else 6.4 fig_y = 4.8 * (1 + 1.0* (n_rows - 1)) if n_rows > 1 and self.plot_helper.n_cols > 1 else 4.8 * (1 + 0.2* (n_rows - 1)) fig, axes = plt.subplots(n_rows, self.plot_helper.n_cols, figsize=(fig_x,fig_y), sharex=self.plot_helper.single_ticks, sharey=self.plot_helper.single_ticks) # add some space between the subplots, to make space for ticks and cbars wspace = 0.4 if self.plot_helper.n_cols > 1 and not self.plot_helper.single_ticks else 0.3 hspace = 0.4 if n_rows > 1 else 0.2 fig.subplots_adjust(hspace=hspace, wspace=wspace) if self.plot_helper.title: fig.suptitle(self.plot_helper.title, fontsize=16) # set the indexes at which subplot the x/y labels should be set if self.plot_helper.n_cols > 1 and n_rows > 1: ids_ylabels = [i for i in range(1, n_subplots+1) if i % self.plot_helper.n_cols == 1] ids_xlabels = [i for i in range(n_subplots-self.plot_helper.n_cols+1, n_subplots+1)] elif self.plot_helper.n_cols == 1 and n_rows > 1: ids_ylabels = [i for i in range(1, n_subplots+1)] ids_xlabels = [n_subplots] elif self.plot_helper.n_cols > 1 and n_rows == 1: ids_ylabels = [1] ids_xlabels = [i for i in range(1, n_subplots+1)] else: ids_xlabels = [1] ids_ylabels = [1] row = 0 col = 0 for sub_id in range(1, n_subplots+1): var_code = var_codes[sub_id-1] # if it is only one subplot, axes is not an array if isinstance(axes, mpl.axes.Axes): ax = axes else: # we need to get the correct axes object, whether it is a 1D or 2D array ax = axes[row, col] if self.plot_helper.n_cols > 1 and n_rows > 1 else axes[row] row += 1 if (sub_id % self.plot_helper.n_cols == 1) or (self.plot_helper.n_cols == 1 or n_rows == 1) else 0 col = (col+1) % self.plot_helper.n_cols cmap = self.plot_helper.getCmap(var_code) norm = self.plot_helper.getNorm(var_code) cmap.set_bad(ax.get_facecolor()) mpl.rcParams['hatch.linewidth'] = 3.0 plot_dates_len = len(plot_dates) for date_id, plot_date in enumerate(plot_dates): # set the bar to the correct date, i.e. the x boundaries x = [plot_date, plot_dates[date_id+1]] if date_id < plot_dates_len-1 else [plot_date, plot_date + pd.Timedelta(days=1)] profile = self.snowpack.get_profile_on(plot_date) # get the layer boundaries, which will be the y boundaries h = profile.get_param("0501") # "0501" are the layer boundaries # the data in the cells data = profile.get_param(var_code, return_missing=True) data = self.plot_helper.handleData(var_code, data) if h.size <= 1: continue # we need to handle missing values mesh = np.array([data,]) conditions = mesh== -999 masked_mesh = np.ma.masked_where(conditions, mesh) cplot = ax.pcolormesh(x, h, masked_mesh.transpose(), norm=norm, cmap=cmap, **kwargs) if var_code == "0513": # this function will add the crust lines to the plot, if it any is set to true _add_crust_lines(ax, x, h, mesh, self.plot_helper.ind_mfcrust, self.plot_helper.mfcrust_color) # set the x and y limits, for better visualization ax.set_xlim(self.plot_helper.start, self.plot_helper.stop) hmax = self.snowpack.get_profile_on(self.plot_helper.stop).get_param("0501")[-1] ymax = hmax if hmax > 250 else 250 ymin,_ = ax.get_ylim() ax.set_ylim(ymin, ymax) self._setColorbar(ax, cplot, var_code, fig) self._set_title(ax, var_code) # set xticks if (self.plot_helper.single_ticks and sub_id in ids_xlabels) or not self.plot_helper.single_ticks: if self.plot_helper.num_ticks: ax.set_xticks(tick_dates) labels = [mdates.num2date(label).strftime('%Y-%m-%d') for label in ax.get_xticks()] ax.set_xticklabels(labels, rotation=30) else: custom_formatter = CustomFormatter('%m-%d', '%m-%d\n%Y') ax.xaxis.set_major_formatter(ticker.FuncFormatter(custom_formatter)) ax.set_xlabel("Date") if (self.plot_helper.single_ticks and sub_id in ids_ylabels) or not self.plot_helper.single_ticks and self.plot_helper.set_ylabel: ax.set_ylabel("HS [cm]") self.latest_fig = fig plt.close(fig) return self.latest_fig def plotProfileOn(self, date:-
Expand source code
def plotProfileOn(self, date:datetime, **kwargs): profile = self.snowpack.get_profile_on(date) self.latest_fig = plotProfile(profile, **kwargs) return self.latest_fig def save(self, outfile: str = None)-
Save the latest figure to an output file.
Args
outfile:str, optional- The path to the output file. If not provided, the figure will be saved to the default savefile specified during object initialization.
Raises
ValueError- If no output file is provided.
Expand source code
def save(self, outfile: str = None): """ Save the latest figure to an output file. Args: outfile (str, optional): The path to the output file. If not provided, the figure will be saved to the default savefile specified during object initialization. Raises: ValueError: If no output file is provided. """ if outfile: self.latest_fig.savefig(outfile) elif self.savefile: self.latest_fig.savefig(self.savefile) else: raise ValueError("No output file provided") def show(self)-
Displays the latest figure of the snowpack.
Expand source code
def show(self): """ Displays the latest figure of the snowpack. """ show_figure(self.latest_fig)