import pandas as pd
import sys
import warnings
from .utils_dynamics import *
from .utils import despline, _matplotlib_points, _datashade_points, _select_font_color
from .utils import arrowed_spines, despline_all, deaxis_all
from .utils import quiver_autoscaler, default_quiver_args
from .utils import save_fig
from .scatters import scatters
from ..estimation.csc.velocity import sol_u, sol_s, solve_first_order_deg
from ..estimation.tsc.utils_moments import moments
from ..tools.utils import get_mapper, log1p_, update_dict, get_valid_bools, index_gene
from ..configuration import _themes


def phase_portraits(
        adata,
        genes,
        x=0,
        y=1,
        pointsize=None,
        vkey=None,
        ekey=None,
        basis="umap",
        log1p=True, 
        color='cell_cycle_phase',
        use_smoothed=True,
        highlights=None,
        discrete_continous_div_themes=None,
        discrete_continous_div_cmap=None,
        discrete_continous_div_color_key=[None, None, None],
        discrete_continous_div_color_key_cmap=None,
        figsize=(6, 4),
        ncols=None,
        legend="upper left",
        background=None,
        show_quiver=False,
        quiver_size=None,
        quiver_length=None,
        frontier=True,
        q_kwargs_dict={},
        show_arrowed_spines=None,
        save_show_or_return='show',
        save_kwargs={},
        **kwargs,
):
    """Draw the phase portrait, expression values , velocity on the low dimensional embedding.
    Note that this function allows to manually set the theme, cmap, color_key and color_key_cmap
    for the phase portrait, expression and velocity subplots. When the background is 'black',
    the default themes for each of those subplots are  ["glasbey_dark", "inferno", "div_blue_black_red"],
    respectively. When the background is 'black', the default themes are  "glasbey_white", "viridis",
    "div_blue_red".

    Parameters
    ----------
        adata: :class:`~anndata.AnnData`
            an Annodata object
        genes: `list`
            A list of gene names that are going to be visualized.
        x: `int` (default: `0`)
                The column index of the low dimensional embedding for the x-axis
        y: `int` (default: `1`)
                The column index of the low dimensional embedding for the y-axis
        pointsize: `None` or `float` (default: None)
                The scale of the point size. Actual point cell size is calculated as `2500.0 / np.sqrt(adata.shape[0]) *
                pointsize`
        vkey: `string` or None (default: None)
            Which velocity key used for visualizing the magnitude of velocity. Can be either velocity in the layers slot
            or the keys in the obsm slot.
        ekey: `str` or None (default: `None`)
            The layer of data to represent the gene expression level.
        basis: `string` (default: umap)
            Which low dimensional embedding will be used to visualize the cell.
        log1p: `bool` (default: `True`)
            Whether to log1p transform the expression so that visualization can be robust to extreme values.
        color: `string` (default: 'cell_cycle_phase')
            Which group will be used to color cells, only used for the phase portrait because the other two plots are
            colored by the velocity magnitude or the gene expression value, respectively.
        use_smoothed: `bool` (default: `True`)
            Whether to use smoothed 1/2-nd moments as gene expression for the first and third columns. This is useful
            for checking the confidence of transition genes. For example, you may see a very nice linear pattern for
            some genes with the smoothed expression but this could just be an artificially generated when the number of
            expressed cells is very low. This raises red flags for the quality of the velocity values we learned for
            those genes. And we recommend to set the higher values (for example, 10% of all cells) for `min_cell_s`,
            `min_cell_u` or `shared_count` of the `fg_kwargs` argument of the dyn.pl.receipe_monocle. Note that this is
            often related to the small single cell datasets (like plate-based scRNA-seq or scSLAM-seq/NASC-seq, etc).
        highlights: `list` (default: None)
            Which color group will be highlighted. if highligts is a list of lists - each list is relate to each color
            element.
        discrete_continous_div_themes: `list[str, str, str]` (optional, default None)
            The discrete, continous and divergent color themes to use for plotting. The description for each element in
            the list is as following.
            A small set of predefined themes are provided which have relatively good aesthetics. Available themes are:
               * 'blue'
               * 'red'
               * 'green'
               * 'inferno'
               * 'fire'
               * 'viridis'
               * 'darkblue'
               * 'darkred'
               * 'darkgreen'
        discrete_continous_div_cmap: `list[str, str, str]`  (optional, default 'Blues')
            The names of  discrete, continous and divergent matplotlib colormap to use for coloring
            or shading points. The description for each element in the list is as following. If no labels or values are
            passed this will be used for shading points according to density (largely only of relevance for very large
            datasets). If values are passed this will be used for shading according the value. Note that if theme is
            passed then this value will be overridden by the corresponding option of the theme.
        discrete_continous_div_color_key: `list[dict or array,, dict or array,, dict or array,]` (default [None, None,
            None]).
            The description for each element in the list is as following. The shape (n_categories) (optional, default
            None). A list to assign discrete, continous and divergent colors to categoricals. This can either be an
            explicit dict mapping labels to colors (as strings of form '#RRGGBB'), or an array like object providing one
            color for each distinct category being provided in ``labels``. Either way this mapping will be used to color
            points according to the label. Note that if theme is passed then this value will be overridden by the
            corresponding option of the theme.
        discrete_continous_div_color_key_cmap: `list[str, str, str]`, (optional, default 'Spectral')
            The names of discrete, continous and divergent matplotlib colormap to use for categorical coloring.
            The description for each element in the list is as following. If an explicit ``color_key`` is not given a
            color mapping for categories can be generated from the label list and selecting a matching list of colors
            from the given colormap. Note that if theme is passed then this value will be overridden by the corresponding
            option of the theme.
        figsize: `None` or `[float, float]` (default: None)
                The width and height of each panel in the figure.
        ncols: `None` or `int` (default: None)
        ncol: `None` or `int` (default: None)
                Number of columns in each facet grid.
        legend: `str` (default: `on data`)
                Where to put the legend.  Legend is drawn by seaborn with “brief” mode, numeric hue and size variables
                will be represented with a sample of evenly spaced values. By default legend is drawn on top of cells.
        show_quiver: `bool` (default: False)
            Whether to show the quiver plot. If velocity for x component (corresponds to either spliced, total RNA,
            protein, etc) or y component (corresponds to either unspliced, new RNA, protein, etc) are both calculated,
            quiver represents velocity for both components otherwise the uncalculated component (usually y component)
            will be set to be 0.
        quiver_size: `float` or None (default: None)
            The size of quiver. If None, we will use set quiver_size to be 1. Note that quiver quiver_size is used to
            calculate the head_width (10 x quiver_size), head_length (12 x quiver_size) and headaxislength (8 x
            quiver_size) of the quiver. This is done via the `default_quiver_args` function which also calculate the
            scale of the quiver (1 / quiver_length).
        quiver_length: `float` or None (default: None)
            The length of quiver. The quiver length which will be used to calculate scale of quiver. Note that befoe
            applying `default_quiver_args` velocity values are first rescaled via the quiver_autoscaler function. Scale
            of quiver indicates the nuumber of data units per arrow length unit, e.g., m/s per plot width; a smaller
            scale parameter makes the arrow longer.
        q_kwargs_dict: `dict` (default: {})
            The dictionary of the quiver arguments. The default setting of quiver argument is identical to that used in
            the cell_wise_velocity and grid_velocity.
        show_arrowed_spines: bool or None (optional, default None)
            Whether to show a pair of arrowed spines represeenting the basis of the scatter is currently using. If None,
            only the first panel in the expression / velocity plot will have the arrowed spine.
        save_show_or_return: {'show', 'save', 'return'} (default: `show`)
            Whether to save, show or return the figure.
        save_kwargs: `dict` (default: `{}`)
            A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig
            function will use the {"path": None, "prefix": 'phase_portraits', "dpi": None, "ext": 'pdf', "transparent":
            True, "close": True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly
            modify those keys according to your needs.
        **kwargs:
            Additional parameters that will be passed to plt.scatter function

    Returns
    -------
        A matplotlib plot that shows 1) the phase portrait of each category used in velocity embedding, cells' low
        dimensional embedding, colored either by 2) the gene expression level or 3) the velocity magnitude values.
    """

    import matplotlib.pyplot as plt
    from matplotlib import rcParams
    from matplotlib.colors import to_hex
    # from matplotlib.colors import DivergingNorm  # TwoSlopeNorm

    has_labeling, has_splicing = adata.uns['dynamics']['has_labeling'], adata.uns['dynamics']['has_splicing']
    if background is None:
        _background = rcParams.get("figure.facecolor")
        _background = to_hex(_background) if type(_background) is tuple else _background
    else:
        _background = background

    mapper = get_mapper(smoothed=use_smoothed)

    point_size = (
        500.0 / np.sqrt(adata.shape[0]) * 5
        if pointsize is None
        else 500.0 / np.sqrt(adata.shape[0]) * 5 * pointsize
    )
    scatter_kwargs = dict(
        alpha=0.2, s=point_size, edgecolor=None, linewidth=0
    )  # (0, 0, 0, 1)

    if kwargs is not None:
        scatter_kwargs.update(kwargs)
    div_scatter_kwargs = scatter_kwargs.copy()
    # div_scatter_kwargs.update({"norm": DivergingNorm(vcenter=0)})

    if type(genes) == str:
        genes = [genes]
    _genes = list(set(adata.var.index).intersection(genes))

    # avoid object for dtype in the gamma column https://stackoverflow.com/questions/40809503/python-numpy-typeerror-ufunc-isfinite-not-supported-for-the-input-types
    k_name = 'gamma_k' if adata.uns['dynamics']['experiment_type'] == 'one-shot' else 'gamma'

    valid_id = np.isfinite(
        np.array(adata.var.loc[_genes, k_name], dtype="float")
    ).flatten()
    genes = np.array(_genes)[valid_id].tolist()
    # idx = [adata.var.index.to_list().index(i) for i in genes]

    if len(genes) == 0:
        raise Exception(
            "adata has no genes listed in your input gene vector or "
            "velocity estimation for those genes are not performed. "
            "Please try to run dyn.tl.dynamics(adata, filter_gene_mode='no')"
            "to estimate velocity for all genes: {}".format(_genes)
        )

    if not "X_" + basis in adata.obsm.keys():
        warnings.warn("{} is not applied to adata.".format(basis))
        from ..tools.dimension_reduction import reduceDimension
        reduceDimension(adata, reduction_method=basis)

    embedding = pd.DataFrame(
        {
            basis + "_0": adata.obsm["X_" + basis][:, x],
            basis + "_1": adata.obsm["X_" + basis][:, y],
        }
    )
    embedding.columns = ["dim_1", "dim_2"]

    if has_labeling and not has_splicing:
        mode = 'labeling'
        vkey = 'T' if vkey is None else vkey
        ekey = 'M_t' if ekey is None else ekey
    elif has_splicing and not has_labeling:
        mode = 'splicing'
        vkey = 'S' if vkey is None else vkey
        ekey = 'M_s' if ekey is None else ekey
    elif has_splicing and has_labeling:
        mode = 'full'
        vkey = 'T' if vkey is None else vkey
        ekey = 'M_t' if ekey is None else ekey

    layers = list(adata.layers.keys())
    layers.extend(["X", "protein", "X_protein"])
    if ekey in layers:
        if ekey == "X":
            E_vec = (
                index_gene(adata, adata.layers[mapper['X']], genes)
                if mapper["X"] in adata.layers.keys()
                else index_gene(adata, adata.X, genes)
            )
        elif ekey in ["protein", "X_protein"]:
            E_vec = (
                index_gene(adata, adata.layers[mapper[ekey]], genes)
                if (ekey in mapper.keys()) and (mapper[ekey] in adata.obsm_keys())
                else index_gene(adata, adata.obsm[ekey], genes)
            )
        else:
            E_vec = (
                index_gene(adata, adata.layers[mapper[ekey]], genes)
                if (ekey in mapper.keys()) and (mapper[ekey] in adata.layers.keys())
                else index_gene(adata, adata.layers[ekey], genes)
            )
        
        if log1p: E_vec = log1p_(adata, E_vec)

    n_cells, n_genes = adata.shape[0], len(genes)

    color_vec = np.repeat(np.nan, n_cells)
    if color is not None:
        color_vec = adata.obs[color].to_list()

    if "velocity_" not in vkey:
        vkey = "velocity_" + vkey
    if vkey == "velocity_U":
        V_vec = index_gene(adata, adata.layers['velocity_U'], genes)
        if "velocity_P" in adata.obsm.keys():
            P_vec = index_gene(adata, adata.layers['velocity_P'], genes)
    elif vkey == "velocity_S":
        V_vec = index_gene(adata, adata.layers['velocity_S'], genes)
        if "velocity_P" in adata.obsm.keys():
            P_vec = index_gene(adata, adata.layers['velocity_P'], genes)
    elif vkey == "velocity_T":
        V_vec = index_gene(adata, adata.layers['velocity_T'], genes)
        if "velocity_P" in adata.obsm.keys():
            P_vec = index_gene(adata, adata.layers['velocity_P'], genes)
    else:
        raise Exception(
            "adata has no vkey {} in either the layers or the obsm slot".format(vkey)
        )

    E_vec, V_vec = (
        E_vec.A if issparse(E_vec) else E_vec,
        V_vec.A if issparse(V_vec) else V_vec,
    )

    if k_name in adata.var.columns:
        if (
                not ("gamma_b" in adata.var.columns)
                or all(adata.var.gamma_b.isna())
        ):
            adata.var.loc[:, "gamma_b"] = 0
        gamma, velocity_offset = (
            index_gene(adata, adata.var.loc[:, k_name].values, genes),
            index_gene(adata, adata.var.gamma_b.values, genes)
        )
        (
            gamma[~np.isfinite(list(gamma))],
            velocity_offset[~np.isfinite(list(velocity_offset))],
        ) = (0, 0)
    else:
        raise Exception(
            "adata does not seem to have velocity_gamma column. Velocity estimation is required before "
            "running this function."
        )

    if mode == "labeling":
        new_mat, tot_mat = (
            index_gene(adata, adata.layers[mapper["X_new"]], genes),
            index_gene(adata, adata.layers[mapper["X_total"]], genes)
        )

        new_mat, tot_mat = (
            (new_mat.A, tot_mat.A) if issparse(new_mat) else (new_mat, tot_mat)
        )

        vel_u, vel_s = (
            index_gene(adata, adata.layers["velocity_N"].A, genes),
            index_gene(adata, adata.layers["velocity_T"].A, genes)
        )

        df = pd.DataFrame(
            {
                "new": new_mat.flatten(),
                "total": tot_mat.flatten(),
                "gene": genes * n_cells,
                "gamma": np.tile(gamma, n_cells),
                "velocity_offset": np.tile(velocity_offset, n_cells),
                "expression": E_vec.flatten(),
                "velocity": V_vec.flatten(),
                "color": np.repeat(color_vec, n_genes),
                "vel_u": vel_u.flatten(),
                "vel_s": vel_s.flatten(),
            },
            index=range(n_cells * n_genes),
        )

    elif mode == "splicing":
        unspliced_mat, spliced_mat = (
            index_gene(adata, adata.layers[mapper["X_unspliced"]], genes),
            index_gene(adata, adata.layers[mapper["X_spliced"]], genes)
        )
                
        unspliced_mat, spliced_mat = (
            (unspliced_mat.A, spliced_mat.A)
            if issparse(unspliced_mat)
            else (unspliced_mat, spliced_mat)
        )

        vel_u, vel_s = (
            np.zeros_like(index_gene(adata, adata.layers["velocity_S"].A, genes)),
            index_gene(adata, adata.layers["velocity_S"].A, genes)
        )

        df = pd.DataFrame(
            {
                "unspliced": unspliced_mat.flatten(),
                "spliced": spliced_mat.flatten(),
                "gene": genes * n_cells,
                "gamma": np.tile(gamma, n_cells),
                "velocity_offset": np.tile(velocity_offset, n_cells),
                "expression": E_vec.flatten(),
                "velocity": V_vec.flatten(),
                "color": np.repeat(color_vec, n_genes),
                "vel_u": vel_u.flatten(),
                "vel_s": vel_s.flatten(),
            },
            index=range(n_cells * n_genes),
        )

    elif mode == "full":
        uu, ul, su, sl = (
            index_gene(adata, adata.layers[mapper["X_uu"]], genes),
            index_gene(adata, adata.layers[mapper["X_ul"]], genes),
            index_gene(adata, adata.layers[mapper["X_su"]], genes),
            index_gene(adata, adata.layers[mapper["X_sl"]], genes),
        )

        vel_u, vel_s = (
            index_gene(adata, adata.layers["velocity_U"].A, genes),
            index_gene(adata, adata.layers["velocity_S"].A, genes),
        ) if vkey == 'velocity_S' else (
            index_gene(adata, adata.layers["velocity_N"].A, genes),
            index_gene(adata, adata.layers["velocity_T"].A, genes),
        )
        if "protein" in adata.obsm.keys():
            if "delta" in adata.var.columns:
                gamma_P = adata.var.delta[genes].values
                velocity_offset_P = (
                    [0] * n_cells
                    if (
                            not ("delta_b" in adata.var.columns)
                            or adata.var.delta_b.unique() is None
                    )
                    else adata.var.delta_b[genes].values
                )
            else:
                raise Exception(
                    "adata does not seem to have velocity_gamma column. Velocity estimation is required before "
                    "running this function."
                )

            P = (
                index_gene(adata, adata.obsm[mapper["X_protein"]], genes)
                if (
                        ["X_protein"] in adata.obsm.keys()
                        or [mapper["X_protein"]] in adata.obsm.keys()
                )
                else index_gene(adata, adata.obsm["protein"], genes)
            )
            uu, ul, su, sl, P = (
                (uu.A, ul.A, su.A, sl.A, P.A) if issparse(uu) else (uu, ul, su, sl, P)
            )
            if issparse(P_vec):
                P_vec = P_vec.A

            vel_p = np.zeros_like(adata.obsm["velocity_P"][:, :])

            # df = pd.DataFrame({"uu": uu.flatten(), "ul": ul.flatten(), "su": su.flatten(), "sl": sl.flatten(), "P": P.flatten(),
            #                    'gene': genes * n_cells, 'prediction': np.tile(gamma, n_cells) * uu.flatten() +
            #                     np.tile(velocity_offset, n_cells), "velocity": genes * n_cells}, index=range(n_cells * n_genes))
            df = pd.DataFrame(
                {
                    "new": (ul + sl).flatten(),
                    "total": (uu + ul + sl + su).flatten(),
                    "S": (sl + su).flatten(),
                    "P": P.flatten(),
                    "gene": genes * n_cells,
                    "gamma": np.tile(gamma, n_cells),
                    "velocity_offset": np.tile(velocity_offset, n_cells),
                    "gamma_P": np.tile(gamma_P, n_cells),
                    "velocity_offset_P": np.tile(velocity_offset_P, n_cells),
                    "expression": E_vec.flatten(),
                    "velocity": V_vec.flatten(),
                    "velocity_protein": P_vec.flatten(),
                    "color": np.repeat(color_vec, n_genes),
                    "vel_u": vel_u.flatten(),
                    "vel_s": vel_s.flatten(),
                    "vel_p": vel_p.flatten(),
                },
                index=range(n_cells * n_genes),
            )
        else:
            df = pd.DataFrame(
                {
                    "new": (ul + sl).flatten(),
                    "total": (uu + ul + sl + su).flatten(),
                    "gene": genes * n_cells,
                    "gamma": np.tile(gamma, n_cells),
                    "velocity_offset": np.tile(velocity_offset, n_cells),
                    "expression": E_vec.flatten(),
                    "velocity": V_vec.flatten(),
                    "color": np.repeat(color_vec, n_genes),
                    "vel_u": vel_u.flatten(),
                    "vel_s": vel_s.flatten(),
                },
                index=range(n_cells * n_genes),
            )
    else:
        raise Exception(
            "Your adata is corrupted. Make sure that your layer has keys new, old for the labelling mode, "
            "spliced, ambiguous, unspliced for the splicing model and uu, ul, su, sl for the full mode"
        )

    num_per_gene = 6 if ("protein" in adata.obsm.keys() and mode == "full") else 3
    ncols = min([num_per_gene, ncols]) if ncols is not None else num_per_gene
    nrow, ncol = int(np.ceil(num_per_gene * n_genes / ncols)), ncols
    if figsize is None:
        g = plt.figure(None, (3 * ncol, 3 * nrow), facecolor=_background)  # , dpi=160
    else:
        g = plt.figure(None, (figsize[0] * ncol, figsize[1] * nrow), facecolor=_background)  # , dpi=160

    if discrete_continous_div_themes is None:
        if _background in ["#ffffff", "black"]:
            discrete_theme, continous_theme, divergent_theme = (
                "glasbey_dark",
                "inferno",
                "div_blue_black_red",
            )
        else:
            discrete_theme, continous_theme, divergent_theme = (
                "glasbey_white",
                "viridis",
                "div_blue_red",
            )
    else:
        discrete_theme, continous_theme, divergent_theme = discrete_continous_div_themes

    discrete_cmap, discrete_color_key_cmap, discrete_background = (
        _themes[discrete_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[0],
        _themes[discrete_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
        discrete_continous_div_color_key_cmap[0],
        _themes[discrete_theme]["background"],
    )
    continous_cmap, continous_color_key_cmap, continous_background = (
        _themes[continous_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[1],
        _themes[continous_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
        discrete_continous_div_color_key_cmap[1],
        _themes[continous_theme]["background"],
    )
    divergent_cmap, divergent_color_key_cmap, divergent_background = (
        _themes[divergent_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[2],
        _themes[divergent_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
        discrete_continous_div_color_key_cmap[2],
        _themes[divergent_theme]["background"],
    )

    font_color = _select_font_color(discrete_background)

    # the following code is inspired by https://github.com/velocyto-team/velocyto-notebooks/blob/master/python/DentateGyrus.ipynb
    gs = plt.GridSpec(nrow, ncol, wspace=0.5, hspace=0.36)
    for i, gn in enumerate(genes):
        if num_per_gene == 3:
            ax1, ax2, ax3 = (
                plt.subplot(gs[i * 3]),
                plt.subplot(gs[i * 3 + 1]),
                plt.subplot(gs[i * 3 + 2]),
            )
        elif num_per_gene == 6:
            ax1, ax2, ax3, ax4, ax5, ax6 = (
                plt.subplot(gs[i * 3]),
                plt.subplot(gs[i * 3 + 1]),
                plt.subplot(gs[i * 3 + 2]),
                plt.subplot(gs[i * 3 + 3]),
                plt.subplot(gs[i * 3 + 4]),
                plt.subplot(gs[i * 3 + 5]),
            )
        try:
            ix = np.where(adata.var.index == gn)[0][0]
        except:
            continue
        cur_pd = df.loc[df.gene == gn, :]
        if cur_pd.color.isna().all():
            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax1, color = _matplotlib_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=None,
                    values=cur_pd.loc[:, "expression"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
            else:
                ax1, color = _datashade_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=None,
                    values=cur_pd.loc[:, "expression"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
        else:
            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax1, color = _matplotlib_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=cur_pd.loc[:, "color"],
                    values=None,
                    highlights=highlights,
                    cmap=discrete_cmap,
                    color_key=discrete_continous_div_color_key[0],
                    color_key_cmap=discrete_color_key_cmap,
                    background=discrete_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
            else:
                ax1, color = _datashade_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=cur_pd.loc[:, "color"],
                    values=None,
                    highlights=highlights,
                    cmap=discrete_cmap,
                    color_key=discrete_continous_div_color_key[0],
                    color_key_cmap=discrete_color_key_cmap,
                    background=discrete_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )

        ax1.set_title(gn)
        ax1.set_xlabel("spliced (1st moment)")
        ax1.set_ylabel("unspliced (1st moment)")
        xnew = np.linspace(0, cur_pd.iloc[:, 1].max() * 0.80)
        ax1.plot(
            xnew,
            xnew * cur_pd.loc[:, "gamma"].unique()
            + cur_pd.loc[:, "velocity_offset"].unique(),
            dashes=[6, 2],
            c=font_color,
        )
        X_array, V_array = (
            cur_pd.iloc[:, [1, 0]].values,
            cur_pd.loc[:, ["vel_s", "vel_u"]].values,
        )

        # add quiver:
        if show_quiver:
            V_array /= 3 * quiver_autoscaler(X_array, V_array)

            if background is None:
                background = rcParams.get("figure.facecolor")
            if quiver_size is None:
                quiver_size = 1
            if background == "black":
                edgecolors = "white"
            else:
                edgecolors = "black"

            head_w, head_l, ax_l, scale = default_quiver_args(
                quiver_size, quiver_length
            )

            quiver_kwargs = {
                "angles": "xy",
                "scale": scale,
                "scale_units": "xy",
                "width": 0.0005,
                "headwidth": head_w,
                "headlength": head_l,
                "headaxislength": ax_l,
                "minshaft": 1,
                "minlength": 1,
                "pivot": "tail",
                "edgecolors": edgecolors,
                "linewidth": 0.2,
                "color": color,
                "alpha": 1,
                "zorder": 10,
            }
            quiver_kwargs = update_dict(quiver_kwargs, q_kwargs_dict)
            ax1.quiver(
                X_array[:, 0],
                X_array[:, 1],
                V_array[:, 0],
                V_array[:, 1],
                **quiver_kwargs
            )

        ax1.set_xlim(0, np.max(X_array[:, 0]) * 1.02)
        ax1.set_ylim(0, np.max(X_array[:, 1]) * 1.02)

        despline(ax1)  # sns.despline()

        df_embedding = pd.concat([cur_pd, embedding], axis=1)
        V_vec = cur_pd.loc[:, "velocity"]

        # limit = np.nanmax(
        #     np.abs(np.nanpercentile(V_vec, [1, 99]))
        # )  # upper and lowe limit / saturation
        #
        # V_vec = V_vec + limit  # that is: tmp_colorandum - (-limit)
        # V_vec = V_vec / (2 * limit)  # that is: tmp_colorandum / (limit - (-limit))
        # V_vec = np.clip(V_vec, 0, 1)
        if show_arrowed_spines is None:
            show_arrowed_spines_ = True if i == 0 else False
        else:
            show_arrowed_spines_ = show_arrowed_spines

        if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
            ax2, _ = _matplotlib_points(
                embedding.iloc[:, :2].values,
                ax=ax2,
                labels=None,
                values=cur_pd.loc[:, "expression"].values,
                highlights=highlights,
                cmap=continous_cmap,
                color_key=discrete_continous_div_color_key[1],
                color_key_cmap=continous_color_key_cmap,
                background=continous_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                **scatter_kwargs
            )
        else:
            ax2, _ = _datashade_points(
                embedding.iloc[:, :2].values,
                ax=ax2,
                labels=None,
                values=cur_pd.loc[:, "expression"].values,
                highlights=highlights,
                cmap=continous_cmap,
                color_key=discrete_continous_div_color_key[1],
                color_key_cmap=continous_color_key_cmap,
                background=continous_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                **scatter_kwargs
            )

        ax2.set_title(gn + " (" + ekey + ")")
        if show_arrowed_spines_:
            ax2 = arrowed_spines(ax2, basis)
        else:
            despline_all(ax2)
            deaxis_all(ax2)

        v_max = 0.01 if min(V_vec) + max(V_vec) == 0 else np.max(np.abs(V_vec.values))
        div_scatter_kwargs.update({"vmin": -v_max, "vmax": v_max})

        if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
            ax3, _ = _matplotlib_points(
                embedding.iloc[:, :2].values,
                ax=ax3,
                labels=None,
                values=V_vec.values,
                highlights=highlights,
                cmap=divergent_cmap,
                color_key=discrete_continous_div_color_key[2],
                color_key_cmap=divergent_color_key_cmap,
                background=divergent_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                sort='abs',
                frontier=frontier,
                **div_scatter_kwargs
            )
        else:
            ax3, _ = _datashade_points(
                embedding.iloc[:, :2].values,
                ax=ax3,
                labels=None,
                values=V_vec.values,
                highlights=highlights,
                cmap=divergent_cmap,
                color_key=discrete_continous_div_color_key[2],
                color_key_cmap=divergent_color_key_cmap,
                background=divergent_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                sort='abs',
                frontier=frontier,
                **div_scatter_kwargs
            )

        ax3.set_title(gn + " (" + vkey + ")")
        if show_arrowed_spines_:
            ax3 = arrowed_spines(ax3, basis)
        else:
            despline_all(ax3)
            deaxis_all(ax3)

        if (
                "protein" in adata.obsm.keys()
                and mode == "full"
                and all([i in adata.layers.keys() for i in ["uu", "ul", "su", "sl"]])
        ):
            if cur_pd.color.unique() != np.nan:
                if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                    ax4, color = _matplotlib_points(
                        cur_pd.iloc[:, [3, 2]].values,
                        ax=ax4,
                        labels=None,
                        values=cur_pd.loc[:, "expression"].values,
                        highlights=highlights,
                        cmap=continous_cmap,
                        color_key=discrete_continous_div_color_key[1],
                        color_key_cmap=continous_color_key_cmap,
                        background=continous_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )
                else:
                    ax4, color = _datashade_points(
                        cur_pd.iloc[:, [3, 2]].values,
                        ax=ax4,
                        labels=None,
                        values=cur_pd.loc[:, "expression"].values,
                        highlights=highlights,
                        cmap=continous_cmap,
                        color_key=discrete_continous_div_color_key[1],
                        color_key_cmap=continous_color_key_cmap,
                        background=continous_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )
            else:
                if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                    ax4, color = _matplotlib_points(
                        cur_pd.iloc[:, [1, 0]].values,
                        ax=ax4,
                        labels=cur_pd.loc[:, "color"].values,
                        values=None,
                        highlights=highlights,
                        cmap=discrete_cmap,
                        color_key=discrete_continous_div_color_key[0],
                        color_key_cmap=discrete_color_key_cmap,
                        background=discrete_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )
                else:
                    ax4, color = _datashade_points(
                        cur_pd.iloc[:, [1, 0]].values,
                        ax=ax4,
                        labels=cur_pd.loc[:, "color"].values,
                        values=None,
                        highlights=highlights,
                        cmap=discrete_cmap,
                        color_key=discrete_continous_div_color_key[0],
                        color_key_cmap=discrete_color_key_cmap,
                        background=discrete_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )

            ax4.set_title(gn)
            ax1.set_xlabel("spliced (1st moment)")
            ax1.set_ylabel("protein (1st moment)")

            xnew = np.linspace(0, cur_pd.iloc[:, 3].max())
            ax4.plot(
                xnew,
                xnew * cur_pd.loc[:, "gamma_P"].unique()
                + cur_pd.loc[:, "velocity_offset_P"].unique(),
                dashes=[6, 2],
                c=font_color,
            )
            X_array, V_array = (
                cur_pd.iloc[:, [3, 2]].values,
                cur_pd.loc[:, ["vel_p", "vel_s"]].values,
            )

            # add quiver:
            if show_quiver:
                V_array /= 3 * quiver_autoscaler(X_array, V_array)

                if background is None:
                    background = rcParams.get("figure.facecolor")
                if quiver_size is None:
                    quiver_size = 1
                if background == "black":
                    edgecolors = "white"
                else:
                    edgecolors = "black"

                head_w, head_l, ax_l, scale = default_quiver_args(
                    quiver_size, quiver_length
                )

                quiver_kwargs = {
                    "angles": "xy",
                    "scale": scale,
                    "scale_units": "xy",
                    "width": 0.0005,
                    "headwidth": head_w,
                    "headlength": head_l,
                    "headaxislength": ax_l,
                    "minshaft": 1,
                    "minlength": 1,
                    "pivot": "tail",
                    "edgecolors": edgecolors,
                    "linewidth": 0.2,
                    "color": color,
                    "alpha": 1,
                    "zorder": 10,
                }
                quiver_kwargs = update_dict(quiver_kwargs, q_kwargs_dict)
                ax4.quiver(
                    X_array[:, 0],
                    X_array[:, 1],
                    V_array[:, 0],
                    V_array[:, 1],
                    **quiver_kwargs
                )

            ax4.set_ylim(0, np.max(X_array[:, 0]) * 1.02)
            ax4.set_xlim(0, np.max(X_array[:, 1]) * 1.02)

            despline(ax1)  # sns.despline()

            V_vec = df_embedding.loc[:, "velocity_p"]

            limit = np.nanmax(
                np.abs(np.nanpercentile(V_vec, [1, 99]))
            )  # upper and lowe limit / saturation

            V_vec = V_vec + limit  # that is: tmp_colorandum - (-limit)
            V_vec = V_vec / (2 * limit)  # that is: tmp_colorandum / (limit - (-limit))
            V_vec = np.clip(V_vec, 0, 1)

            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax5, _ = _matplotlib_points(
                    embedding.iloc[:, :2],
                    ax=ax5,
                    labels=None,
                    values=embedding.loc[:, "P"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
            else:
                ax5, _ = _datashade_points(
                    embedding.iloc[:, :2],
                    ax=ax5,
                    labels=None,
                    values=embedding.loc[:, "P"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )

            ax5.set_title(gn + " (protein expression)")
            if show_arrowed_spines_:
                ax5 = arrowed_spines(ax5, basis)
            else:
                despline_all(ax5)
                deaxis_all(ax5)

            v_max = np.max(np.abs(V_vec.values))
            div_scatter_kwargs.update({"vmin": -v_max, "vmax": v_max})

            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax6, _ = _matplotlib_points(
                    embedding.iloc[:, :2],
                    ax=ax6,
                    labels=None,
                    values=V_vec.values,
                    highlights=highlights,
                    cmap=divergent_cmap,
                    color_key=discrete_continous_div_color_key[2],
                    color_key_cmap=divergent_color_key_cmap,
                    background=divergent_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    sort='abs',
                    frontier=frontier,
                    **div_scatter_kwargs
                )
            else:
                ax6, _ = _datashade_points(
                    embedding.iloc[:, :2],
                    ax=ax6,
                    labels=None,
                    values=V_vec.values,
                    highlights=highlights,
                    cmap=divergent_cmap,
                    color_key=discrete_continous_div_color_key[2],
                    color_key_cmap=divergent_color_key_cmap,
                    background=divergent_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    sort='abs',
                    frontier=frontier,
                    **div_scatter_kwargs
                )

            ax6.set_title(gn + " (protein velocity)")
            if show_arrowed_spines_:
                ax6 = arrowed_spines(ax6, basis)
            else:
                despline_all(ax6)
                deaxis_all(ax6)

    if save_show_or_return == "save":
        s_kwargs = {"path": None, "prefix": 'phase_portraits', "dpi": None,
                    "ext": 'pdf', "transparent": True, "close": True, "verbose": True}
        s_kwargs = update_dict(s_kwargs, save_kwargs)

        save_fig(**s_kwargs)
    elif save_show_or_return == "show":
        
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            plt.tight_layout()
        
        plt.show()
    elif save_show_or_return == "return":
        return g


def dynamics(
        adata,
        genes,
        unit="hours",
        log_unnormalized=True,
        y_log_scale=False,
        ci=None,
        ncols=None,
        figsize=None,
        dpi=None,
        boxwidth=None,
        barwidth=None,
        true_param_prefix=None,
        show_moms_fit=False,
        show_variance=True,
        show_kin_parameters=True,
        gene_order='column',
        font_size_scale=1,
        save_show_or_return='show',
        save_kwargs={},
):
    """Plot the data and fitting of different metabolic labeling experiments.
    Note that if non-smoothed data was used for kinetic fitting, you often won't see boxplot
    but only the triangles for the mean values. This is because the raw data has a lot dropout,
    thus the median is outside of the whiskers of the boxplot and the boxplot is then not drawn
    by default.

    Parameters
    ----------
        adata: :class:`~anndata.AnnData`
            an Annodata object.
        genes: list of `str`
            key for variable or gene names.
        unit: `str` (default: `hour`)
            The unit of the labeling time, for example, `hours` or `minutes`.
        y_log_scale: `bool` (default: `True`)
            Whether or not to use log scale for y-axis.
        ci: `str` (for example: "95%") or None (default: `None`)
            The confidence interval to be drawed for the parameter fitting. Currently not used.
        ncols: `int` or None (default: `None`)
            The number of columns in the plot.
        figsize: `[float, float]` or `(float, float)` or None
            The size of the each panel in the figure.
        dpi: `float` or None
            Figure resolution.
        boxwidth: `float`
            The width of the box of the boxplot.
        barwidth: `float`
            The width of the bar of the barplot.
        true_param_prefix: `str`
            The prefix for the column names of true parameters in the .var attributes. Useful for the simulation data.
        show_moms_fit: `bool` (default: `True`)
            Whether to show fitting curves associated with the stochastic models, only works for non-deterministic models.
        show_variance: `bool` (default: `True`)
            Whether to add a boxplot to show the variance at each time point.
        show_kin_parameters: `bool` (default: `True`)
            Whether to include the estimated kinetic parameter values on the plot.
        gene_order: `str` (default: `column`)
            The order of genes to present on the figure, either row-major or column major.
        font_size_scale: `float` (default: `1`)
            A value that will be used for scaling
        save_show_or_return: {'show', 'save', 'return'} (default: `show`)
            Whether to save, show or return the figure.
        save_kwargs: `dict` (default: `{}`)
            A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function
            will use the {"path": None, "prefix": 'dynamics', "dpi": None, "ext": 'pdf', "transparent": True, "close":
            True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys
            according to your needs.

    Returns
    -------
        Nothing but plot the figure of the metabolic fitting.
    """

    import matplotlib.pyplot as plt
    import matplotlib
    params = {'font.size': 4 * font_size_scale,
              'legend.fontsize': 4 * font_size_scale,
              'legend.handlelength': 0.5,
              'axes.labelsize': 6 * font_size_scale,
              'axes.titlesize': 6 * font_size_scale,
              'xtick.labelsize': 6 * font_size_scale,
              'ytick.labelsize': 6 * font_size_scale,
              'axes.titlepad': 1,
              'axes.labelpad': 1,
              'axes.spines.right': False,
              'axes.spines.top': False
              }
    matplotlib.rcParams.update(params)

    show_kin_parameters = True if true_param_prefix else show_kin_parameters

    uns_keys = np.array(adata.uns_keys())
    tmp = np.array([i.split("_dynamics")[0] if i.endswith('_dynamics') else None for i in uns_keys])
    tmp1 = [False if i is None else True for i in tmp]
    group = tmp[tmp1][0] if sum(tmp1) > 0 else None

    if group is not None:
        uns_key = group + "_dynamics"
        _group, grp_len = np.unique(adata.obs[group]), len(np.unique(adata.obs[group]))
    else:
        uns_key = "dynamics"
        _group, grp_len = ["_all_cells"], 1

    experiment_type = adata.uns[uns_key]['experiment_type']
    T = adata.uns[uns_key]['t']
    filter_gene_mode = adata.uns[uns_key]['filter_gene_mode']
    X_data = adata.uns[uns_key]['X_data']
    X_fit_data = adata.uns[uns_key]['X_fit_data']
    has_splicing = adata.uns[uns_key]['has_splicing']
    model = adata.uns[uns_key]['model']
    use_smoothed = adata.uns[uns_key]['use_smoothed']

    # for key, val in adata.uns[uns_key].items():
    #     exec(key + '=val')

    if experiment_type == "conventional":
        # run the phase_portraits plot
        warnings.warn(
            "dynamics plot doesn't support conventional experiment type, using phase_portraits function instead."
        )
        phase_portraits(adata)

    T_uniq = np.unique(T)
    t = np.linspace(0, T_uniq[-1], 1000)
    valid_genes = adata.var_names[get_valid_bools(adata, filter_gene_mode)]
    valid_gene_names = valid_genes.intersection(genes)

    if len(valid_gene_names) == 0:
        raise ValueError(f"no kinetic parameter fitting has performed for the gene {genes} you provided. Try "
                         f"using dyn.tl.dynamics(adata, filter_gene_mode='final', ...) or use only fitted genes.")

    valid_adata = adata[:, valid_gene_names]
    gene_idx = np.array([np.where(valid_genes == gene)[0][0] for gene in valid_gene_names])
    X_data, X_fit_data = [X_data[i] for i in gene_idx], [X_fit_data[i] for i in gene_idx]

    if boxwidth is None and len(T_uniq) > 1:
        boxwidth = 0.8 * (np.diff(T_uniq).min() / 2)

    if barwidth is None and len(T_uniq) > 1:
        barwidth = 0.8 * (np.diff(T_uniq).min() / 2)

    if has_splicing:
        if model == "moment":
            sub_plot_n = 4  # number of subplots for each gene
        elif experiment_type == "kin":
            if model == 'deterministic':
                sub_plot_n = 2 if show_variance else 1
            elif model == 'stochastic':
                mom_n = 3 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
            elif model == 'mixture':
                sub_plot_n = 4 if show_variance else 2
            elif model == 'mixture_deterministic_stochastic':
                mom_n = 3 if show_moms_fit else 0
                sub_plot_n = 4 + mom_n if show_variance else 2 + mom_n
            elif model == 'mixture_stochastic_stochastic':
                mom_n = 6 if show_moms_fit else 0
                sub_plot_n = 4 + mom_n if show_variance else 2 + mom_n
        elif experiment_type == "deg":
            if model == 'deterministic':
                sub_plot_n = 2 if show_variance else 1
            elif model == 'stochastic':
                mom_n = 3 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
        elif experiment_type == "one_shot":  # just the labeled RNA
            sub_plot_n = 1
        elif experiment_type == "mix_std_stm":
            sub_plot_n = 5
    else:
        if model == "moment":
            sub_plot_n = 2  # number of subplots for each gene
        elif experiment_type == "kin":
            if model == 'deterministic':
                sub_plot_n = 1
            elif model == 'stochastic':
                mom_n = 1 if show_moms_fit else 0
                sub_plot_n = 1 + mom_n if show_variance else 1 + mom_n
            elif model == 'mixture':
                sub_plot_n = 2 if show_variance else 1
            elif model == 'mixture_deterministic_stochastic':
                mom_n = 1 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
            elif model == 'mixture_stochastic_stochastic':
                mom_n = 2 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
        elif experiment_type == "deg":
            if model == 'deterministic':
                sub_plot_n = 1
            elif model == 'stochastic':
                mom_n = 1 if show_moms_fit else 0
                sub_plot_n = 1 + mom_n
        elif experiment_type == "one_shot":  # just the labeled RNA
            sub_plot_n = 1
        elif experiment_type == "mix_std_stm":
            sub_plot_n = 3

    ncols = ( # each column correspond to one gene
        len(gene_idx) * grp_len
        if ncols is None
        else min(len(gene_idx) * grp_len, ncols)
    )
    nrows = int(np.ceil(len(gene_idx) * sub_plot_n * grp_len / ncols))
    figsize = [7, 5] if figsize is None else figsize
    g = plt.figure(None, (figsize[0] * ncols, figsize[1] * nrows), dpi=dpi)
    gs = plt.GridSpec(
        nrows,
        ncols,
        g,
        wspace=0.12,
    )

    # there are bugs when fig is not a symmetric matrix
    fig_mat = np.arange(nrows * ncols).reshape((nrows, ncols))

    # we need to visualize gene in row-wise mode
    for grp_idx, cur_grp in enumerate(_group):
        if cur_grp == "_all_cells":
            prefix = ""  # kinetic_parameter_
        else:
            prefix = group + "_" + cur_grp + "_"

        true_p = None;
        true_params = [None, None, None]
        logLL = valid_adata.var.loc[valid_gene_names, prefix + 'logLL']
        est_params_df = valid_adata.var.loc[
            valid_gene_names,
            [
                prefix + "alpha",
                prefix + "beta",
                prefix + "gamma",
                "half_life",
            ],
        ]

        est_params = [est_params_df.loc[:, 'alpha'].values, est_params_df.loc[:, 'beta'].values,
                      est_params_df.loc[:, 'gamma'].values]

        if experiment_type == "kin":
            if model == 'deterministic':
                gs = plot_kin_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_variance, show_kin_parameters, )
            elif model == 'stochastic':
                gs = plot_kin_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
            elif model == 'mixture':
                gs = plot_kin_mix(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_variance, show_kin_parameters, )
            elif model == 'mixture_deterministic_stochastic':
                gs = plot_kin_mix_det_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
            elif model == 'mixture_stochastic_stochastic':
                gs = plot_kin_mix_sto_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
        elif experiment_type == "deg":
            if model == 'deterministic':
                gs = plot_deg_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_variance, show_kin_parameters, )
            elif model == 'stochastic':
                gs = plot_deg_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
        else:
            for i, gene_name in enumerate(valid_genes):
                if model == "moment":
                    a, b, alpha_a, alpha_i, beta, gamma = valid_adata.var.loc[
                        gene_name,
                        [
                            prefix + "a",
                            prefix + "b",
                            prefix + "alpha_a",
                            prefix + "alpha_i",
                            prefix + "beta",
                            prefix + "gamma",
                        ],
                    ]
                    params = {
                        "a": a,
                        "b": b,
                        "alpha_a": alpha_a,
                        "alpha_i": alpha_i,
                        "beta": beta,
                        "gamma": gamma,
                    }  # "la": 1, "si": 0,
                    mom = moments(*list(params.values()))
                    mom.integrate(t)
                    mom_data = (
                        mom.get_all_central_moments()
                        if has_splicing
                        else mom.get_nosplice_central_moments()
                    )
                    if true_param_prefix is not None:
                        (
                            true_a,
                            true_b,
                            true_alpha_a,
                            true_alpha_i,
                            true_beta,
                            true_gamma,
                        ) = (
                            valid_adata.var.loc[gene_name, true_param_prefix + "a"]
                            if true_param_prefix + "a" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "b"]
                            if true_param_prefix + "b" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "alpha_a"]
                            if true_param_prefix + "alpha_a" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "alpha_i"]
                            if true_param_prefix + "alpha_i" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                            if true_param_prefix + "beta" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                            if true_param_prefix + "gamma" in valid_adata.var_keys()
                            else -np.inf,
                        )

                        true_params = {
                            "a": true_a,
                            "b": true_b,
                            "alpha_a": true_alpha_a,
                            "alpha_i": true_alpha_i,
                            "beta": true_beta,
                            "gamma": true_gamma,
                        }  # "la": 1, "si": 0,
                        true_mom = moments(*list(true_params.values()))
                        true_mom.integrate(t)
                        true_mom_data = (
                            true_mom.get_all_central_moments()
                            if has_splicing
                            else true_mom.get_nosplice_central_moments()
                        )

                    # n_mean, n_var = x_data[:2, :], x_data[2:, :]
                    if has_splicing:
                        tmp = (
                            [
                                valid_adata[:, gene_name].layers["M_ul"].A.T,
                                valid_adata.layers["M_sl"].A.T,
                            ]
                            if "M_ul" in valid_adata.layers.keys()
                            else [
                                valid_adata[:, gene_name].layers["ul"].A.T,
                                valid_adata.layers["sl"].A.T,
                            ]
                        )
                        x_data = [tmp[0].A, tmp[1].A] if issparse(tmp[0]) else tmp
                        if log_unnormalized and "X_ul" not in valid_adata.layers.keys():
                            x_data = [np.log(tmp[0] + 1), np.log(tmp[1] + 1)]

                        title_ = [
                            "(unspliced labeled)",
                            "(spliced labeled)",
                            "(unspliced labeled)",
                            "(spliced labeled)",
                        ]
                        Obs_m = [valid_adata.uns["M_ul"], valid_adata.uns["M_sl"]]
                        Obs_v = [valid_adata.uns["V_ul"], valid_adata.uns["V_sl"]]
                        j_species = 2  # number of species
                    else:
                        tmp = (
                            valid_adata[:, gene_name].layers["X_new"].T
                            if "X_new" in valid_adata.layers.keys()
                            else valid_adata[:, gene_name].layers["new"].T
                        )
                        x_data = [tmp.A] if issparse(tmp) else [tmp]

                        if log_unnormalized and "X_new" not in valid_adata.layers.keys():
                            x_data = [np.log(x_data[0] + 1)]
                        # only use new key for calculation, so we only have M, V
                        title_ = [" (labeled)", " (labeled)"]
                        Obs_m, Obs_v = [valid_adata.uns["M"]], [valid_adata.uns["V"]]
                        j_species = 1

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure all related plots for the same gene in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if true_param_prefix is not None and j == 0:
                            if has_splicing:
                                ax.text(
                                    0.05,
                                    0.92,
                                    r"$a$"
                                    + ": {0:.2f}; ".format(true_a)
                                    + r"$\hat a$"
                                    + ": {0:.2f} \n".format(a)
                                    + r"$b$"
                                    + ": {0:.2f}; ".format(true_b)
                                    + r"$\hat b$"
                                    + ": {0:.2f} \n".format(b)
                                    + r"$\alpha_a$"
                                    + ": {0:.2f}; ".format(true_alpha_a)
                                    + r"$\hat \alpha_a$"
                                    + ": {0:.2f} \n".format(alpha_a)
                                    + r"$\alpha_i$"
                                    + ": {0:.2f}; ".format(true_alpha_i)
                                    + r"$\hat \alpha_i$"
                                    + ": {0:.2f} \n".format(alpha_i)
                                    + r"$\beta$"
                                    + ": {0:.2f}; ".format(true_beta)
                                    + r"$\hat \beta$"
                                    + ": {0:.2f} \n".format(beta)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="left",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                            else:
                                ax.text(
                                    0.05,
                                    0.92,
                                    r"$a$"
                                    + ": {0:.2f}; ".format(true_a)
                                    + r"$\hat a$"
                                    + ": {0:.2f} \n".format(a)
                                    + r"$b$"
                                    + ": {0:.2f}; ".format(true_b)
                                    + r"$\hat b$"
                                    + ": {0:.2f} \n".format(b)
                                    + r"$\alpha_a$"
                                    + ": {0:.2f}; ".format(true_alpha_a)
                                    + r"$\hat \alpha_a$"
                                    + ": {0:.2f} \n".format(alpha_a)
                                    + r"$\alpha_i$"
                                    + ": {0:.2f}; ".format(true_alpha_i)
                                    + r"$\hat \alpha_i$"
                                    + ": {0:.2f} \n".format(alpha_i)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="left",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                        if j < j_species:
                            ax.boxplot(
                                x=[x_data[j][i][T == std] for std in T_uniq],
                                positions=T_uniq,
                                widths=boxwidth,
                                showfliers=False,
                                showmeans=True,
                            )  # x=T.values, y= # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10)
                            # ax.scatter(T_uniq, Obs_m[j][i], c='r')  # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10)
                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Expression (log)")  #
                            else:
                                ax.set_ylabel("Expression")
                            ax.plot(t, mom_data[j], "k--")
                            if true_param_prefix is not None:
                                ax.plot(t, true_mom_data[j], "r--")
                        else:
                            ax.scatter(T_uniq, Obs_v[j - j_species][i])  # , c='r'
                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Variance (log expression)")  #
                            else:
                                ax.set_ylabel("Variance")
                            ax.plot(t, mom_data[j], "k--")
                            if true_param_prefix is not None:
                                ax.plot(t, true_mom_data[j], "r--")

                        ax.set_xlabel("time (" + unit + ")")
                        ax.set_title(gene_name + " " + title_[j])

                elif experiment_type == "deg":
                    if has_splicing:
                        layers = (
                            ["X_uu", "X_ul", "X_su", "X_sl"]
                            if "X_ul" in valid_adata.layers.keys()
                            else ["uu", "ul", "su", "sl"]
                        )
                        uu, ul, su, sl = (
                            valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[1]],
                            valid_adata[:, gene_name].layers[layers[2]],
                            valid_adata[:, gene_name].layers[layers[3]],
                        )
                        uu, ul, su, sl = (
                            (
                                uu.toarray().squeeze(),
                                ul.toarray().squeeze(),
                                su.toarray().squeeze(),
                                sl.toarray().squeeze(),
                            )
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                        )

                        if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                            uu, ul, su, sl = (
                                np.log(uu + 1),
                                np.log(ul + 1),
                                np.log(su + 1),
                                np.log(sl + 1),
                            )

                        alpha, beta, gamma, ul0, sl0, uu0, half_life = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "beta",
                                prefix + "gamma",
                                prefix + "ul0",
                                prefix + "sl0",
                                prefix + "uu0",
                                "half_life",
                            ],
                        ]
                        # $u$ - unlabeled, unspliced
                        # $s$ - unlabeled, spliced
                        # $w$ - labeled, unspliced
                        # $l$ - labeled, spliced
                        #
                        beta = sys.float_info.epsilon if beta == 0 else beta
                        gamma = sys.float_info.epsilon if gamma == 0 else gamma
                        u = sol_u(t, uu0, alpha, beta)
                        su0 = np.mean(su[T == np.min(T)])  # this should also be estimated
                        s = sol_s(t, su0, uu0, alpha, beta, gamma)
                        w = sol_u(t, ul0, 0, beta)
                        l = sol_s(t, sl0, ul0, 0, beta, gamma)
                        if true_param_prefix is not None:
                            true_alpha, true_beta, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                                if true_param_prefix + "beta" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )

                            true_u = sol_u(t, uu0, true_alpha, true_beta)
                            true_s = sol_s(t, su0, uu0, true_alpha, true_beta, true_gamma)
                            true_w = sol_u(t, ul0, 0, true_beta)
                            true_l = sol_s(t, sl0, ul0, 0, true_beta, true_gamma)

                            true_p = np.vstack((true_u, true_w, true_s, true_l))

                        title_ = [
                            "(unspliced unlabeled)",
                            "(unspliced labeled)",
                            "(spliced unlabeled)",
                            "(spliced labeled)",
                        ]

                        Obs, Pred = np.vstack((uu, ul, su, sl)), np.vstack((u, w, s, l))
                    else:
                        layers = (
                            ["X_new", "X_total"]
                            if "X_new" in valid_adata.layers.keys()
                            else ["new", "total"]
                        )
                        uu, ul = (
                            valid_adata[:, gene_name].layers[layers[1]]
                            - valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[0]],
                        )
                        uu, ul = (
                            (uu.toarray().squeeze(), ul.toarray().squeeze())
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze())
                        )

                        if log_unnormalized and layers == ["new", "total"]:
                            uu, ul = np.log(uu + 1), np.log(ul + 1)

                        alpha, gamma, uu0, ul0, half_life = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "gamma",
                                prefix + "uu0",
                                prefix + "ul0",
                                "half_life",
                            ],
                        ]

                        # require no beta functions
                        u = sol_u(t, uu0, alpha, gamma)
                        s = None  # sol_s(t, su0, uu0, 0, 1, gamma)
                        w = sol_u(t, ul0, 0, gamma)
                        l = None  # sol_s(t, 0, 0, alpha, 1, gamma)
                        title_ = ["(unlabeled)", "(labeled)"]
                        if true_param_prefix is not None:
                            true_alpha, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )
                            true_u = sol_u(t, uu0, true_alpha, true_gamma)
                            true_w = sol_u(t, ul0, 0, true_gamma)

                            true_p = np.vstack((true_u, true_w))

                        Obs, Pred = np.vstack((uu, ul)), np.vstack((u, w))

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure unlabled and labeled are in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if true_param_prefix is not None and j == 0:
                            if has_splicing:
                                ax.text(
                                    0.75,
                                    0.50,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\beta$"
                                    + ": {0:.2f}; ".format(true_beta)
                                    + r"$\hat \beta$"
                                    + ": {0:.2f} \n".format(beta)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                            else:
                                ax.text(
                                    0.75,
                                    0.50,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )

                        ax.boxplot(
                            x=[Obs[j][T == std] for std in T_uniq],
                            positions=T_uniq,
                            widths=boxwidth,
                            showfliers=False,
                            showmeans=True,
                        )
                        ax.plot(t, Pred[j], "k--")
                        if true_param_prefix is not None:
                            ax.plot(t, true_p[j], "r--")
                        if j == sub_plot_n - 1:
                            ax.text(
                                0.8,
                                0.8,
                                r"$t_{1/2} = $" + "{0:.2f}".format(half_life) + unit[0],
                                ha="right",
                                va="top",
                                transform=ax.transAxes,
                            )
                        ax.set_xlabel("time (" + unit + ")")
                        if y_log_scale:
                            ax.set_yscale("log")
                        if log_unnormalized:
                            ax.set_ylabel("Expression (log)")
                        else:
                            ax.set_ylabel("Expression")
                        ax.set_title(gene_name + " " + title_[j])
                elif experiment_type == "kin":
                    if model == 'deterministic':
                        logLL = valid_adata.var.loc[valid_gene_names, prefix + 'logLL']
                        alpha, beta, gamma, half_life = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "beta",
                                prefix + "gamma",
                                "half_life",
                            ],
                        ]
                        est_params = [alpha, beta, gamma]
                        gs = plot_kin_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                     t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                     grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                     true_param_prefix, true_params, est_params,
                                     show_variance, show_kin_parameters, )

                    elif model == 'stochastic':
                        if has_splicing:
                            pass
                        else:
                            pass
                    elif model == 'mixture':
                        if has_splicing:
                            pass
                        else:
                            pass
                    elif model == 'mixture_deterministic_stochastic':
                        if has_splicing:
                            pass
                        else:
                            pass
                    elif model == 'mixture_stochastic_stochastic':
                        if has_splicing:
                            pass
                        else:
                            pass
                    else:
                        if has_splicing:
                            layers = (
                                ["X_uu", "X_ul", "X_su", "X_sl"]
                                if "X_ul" in valid_adata.layers.keys()
                                else ["uu", "ul", "su", "sl"]
                            )
                            uu, ul, su, sl = (
                                valid_adata[:, gene_name].layers[layers[0]],
                                valid_adata[:, gene_name].layers[layers[1]],
                                valid_adata[:, gene_name].layers[layers[2]],
                                valid_adata[:, gene_name].layers[layers[3]],
                            )
                            uu, ul, su, sl = (
                                (
                                    uu.toarray().squeeze(),
                                    ul.toarray().squeeze(),
                                    su.toarray().squeeze(),
                                    sl.toarray().squeeze(),
                                )
                                if issparse(uu)
                                else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                            )

                            if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                                uu, ul, su, sl = (
                                    np.log(uu + 1),
                                    np.log(ul + 1),
                                    np.log(su + 1),
                                    np.log(sl + 1),
                                )

                            alpha, beta, gamma, uu0, su0 = valid_adata.var.loc[
                                gene_name,
                                [
                                    prefix + "alpha",
                                    prefix + "beta",
                                    prefix + "gamma",
                                    prefix + "uu0",
                                    prefix + "su0",
                                ],
                            ]
                            # $u$ - unlabeled, unspliced
                            # $s$ - unlabeled, spliced
                            # $w$ - labeled, unspliced
                            # $l$ - labeled, spliced
                            #
                            beta = sys.float_info.epsilon if beta == 0 else beta
                            gamma = sys.float_info.epsilon if gamma == 0 else gamma
                            u = sol_u(t, uu0, 0, beta)
                            s = sol_s(t, su0, uu0, 0, beta, gamma)
                            w = sol_u(t, 0, alpha, beta)
                            l = sol_s(t, 0, 0, alpha, beta, gamma)
                            if true_param_prefix is not None:
                                true_alpha, true_beta, true_gamma = (
                                    valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                    if true_param_prefix + "alpha" in valid_adata.var_keys()
                                    else -np.inf,
                                    valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                                    if true_param_prefix + "beta" in valid_adata.var_keys()
                                    else -np.inf,
                                    valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                    if true_param_prefix + "gamma" in valid_adata.var_keys()
                                    else -np.inf,
                                )
                                true_u = sol_u(t, uu0, 0, true_beta)
                                true_s = sol_s(t, su0, uu0, 0, true_beta, true_gamma)
                                true_w = sol_u(t, 0, true_alpha, true_beta)
                                true_l = sol_s(t, 0, 0, true_alpha, true_beta, true_gamma)

                                true_p = np.vstack((true_u, true_w, true_s, true_l))

                            title_ = [
                                "(unspliced unlabeled)",
                                "(unspliced labeled)",
                                "(spliced unlabeled)",
                                "(spliced labeled)",
                            ]

                            Obs, Pred = np.vstack((uu, ul, su, sl)), np.vstack((u, w, s, l))
                        else:
                            layers = (
                                ["X_new", "X_total"]
                                if "X_new" in valid_adata.layers.keys()
                                else ["new", "total"]
                            )
                            uu, ul = (
                                valid_adata[:, gene_name].layers[layers[1]]
                                - valid_adata[:, gene_name].layers[layers[0]],
                                valid_adata[:, gene_name].layers[layers[0]],
                            )
                            uu, ul = (
                                (uu.toarray().squeeze(), ul.toarray().squeeze())
                                if issparse(uu)
                                else (uu.squeeze(), ul.squeeze())
                            )

                            if log_unnormalized and layers == ["new", "total"]:
                                uu, ul = np.log(uu + 1), np.log(ul + 1)

                            alpha, gamma, uu0 = valid_adata.var.loc[
                                gene_name, [prefix + "alpha", prefix + "gamma", prefix + "uu0"]
                            ]

                            # require no beta functions
                            u = sol_u(t, uu0, 0, gamma)
                            s = None  # sol_s(t, su0, uu0, 0, 1, gamma)
                            w = sol_u(t, 0, alpha, gamma)
                            l = None  # sol_s(t, 0, 0, alpha, 1, gamma)
                            if true_param_prefix is not None:
                                true_alpha, true_gamma = (
                                    valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                    if true_param_prefix + "alpha" in valid_adata.var_keys()
                                    else -np.inf,
                                    valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                    if true_param_prefix + "gamma" in valid_adata.var_keys()
                                    else -np.inf,
                                )
                                true_u = sol_u(t, uu0, 0, true_gamma)
                                true_w = sol_u(t, 0, true_alpha, true_gamma)

                                true_p = np.vstack((true_u, true_w))

                            title_ = ["(unlabeled)", "(labeled)"]

                            Obs, Pred = np.vstack((uu, ul)), np.vstack((u, w))

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure unlabled and labeled are in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if true_param_prefix is not None and j == 0:
                            if has_splicing:
                                ax.text(
                                    0.75,
                                    0.90,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\beta$"
                                    + ": {0:.2f}; ".format(true_beta)
                                    + r"$\hat \beta$"
                                    + ": {0:.2f} \n".format(beta)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                            else:
                                ax.text(
                                    0.75,
                                    0.90,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )

                        ax.boxplot(
                            x=[Obs[j][T == std] for std in T_uniq],
                            positions=T_uniq,
                            widths=boxwidth,
                            showfliers=False,
                            showmeans=True,
                        )
                        ax.plot(t, Pred[j], "k--")
                        if true_param_prefix is not None:
                            ax.plot(t, true_p[j], "k--")
                        ax.set_xlabel("time (" + unit + ")")
                        if y_log_scale:
                            ax.set_yscale("log")
                        if log_unnormalized:
                            ax.set_ylabel("Expression (log)")
                        else:
                            ax.set_ylabel("Expression")
                        ax.set_title(gene_name + " " + title_[j])
                elif experiment_type == "one_shot":
                    if has_splicing:
                        layers = (
                            ["X_uu", "X_ul", "X_su", "X_sl"]
                            if "X_ul" in valid_adata.layers.keys()
                            else ["uu", "ul", "su", "sl"]
                        )
                        uu, ul, su, sl = (
                            valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[1]],
                            valid_adata[:, gene_name].layers[layers[2]],
                            valid_adata[:, gene_name].layers[layers[3]],
                        )
                        uu, ul, su, sl = (
                            (
                                uu.toarray().squeeze(),
                                ul.toarray().squeeze(),
                                su.toarray().squeeze(),
                                sl.toarray().squeeze(),
                            )
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                        )

                        if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                            uu, ul, su, sl = (
                                np.log(uu + 1),
                                np.log(ul + 1),
                                np.log(su + 1),
                                np.log(sl + 1),
                            )

                        alpha, beta, gamma, U0, S0 = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "beta",
                                prefix + "gamma",
                                prefix + "U0",
                                prefix + "S0",
                            ],
                        ]
                        # $u$ - unlabeled, unspliced
                        # $s$ - unlabeled, spliced
                        # $w$ - labeled, unspliced
                        # $l$ - labeled, spliced
                        #
                        beta = sys.float_info.epsilon if beta == 0 else beta
                        gamma = sys.float_info.epsilon if gamma == 0 else gamma
                        U_sol = sol_u(t, U0, 0, beta)
                        S_sol = sol_u(t, S0, 0, gamma)
                        l = sol_u(t, 0, alpha, beta) + sol_s(t, 0, 0, alpha, beta, gamma)
                        L = sl + ul
                        if true_param_prefix is not None:
                            true_alpha, true_beta, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                                if true_param_prefix + "beta" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )
                            true_l = sol_u(t, 0, true_alpha, true_beta) + sol_s(
                                t, 0, 0, true_alpha, true_beta, true_gamma
                            )

                        title_ = ["labeled"]
                    else:
                        layers = (
                            ["X_new", "X_total"]
                            if "X_new" in valid_adata.layers.keys()
                            else ["new", "total"]
                        )
                        uu, ul = (
                            valid_adata[:, gene_name].layers[layers[1]]
                            - valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[0]],
                        )
                        uu, ul = (
                            (uu.toarray().squeeze(), ul.toarray().squeeze())
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze())
                        )

                        if log_unnormalized and layers == ["new", "total"]:
                            uu, ul = np.log(uu + 1), np.log(ul + 1)

                        alpha, gamma, total0 = valid_adata.var.loc[
                            gene_name,
                            [prefix + "alpha", prefix + "gamma", prefix + "total0"],
                        ]

                        # require no beta functions
                        old = sol_u(t, total0, 0, gamma)
                        s = None  # sol_s(t, su0, uu0, 0, 1, gamma)
                        w = None
                        l = sol_u(t, 0, alpha, gamma)  # sol_s(t, 0, 0, alpha, 1, gamma)
                        L = ul
                        if true_param_prefix is not None:
                            true_alpha, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )
                            true_l = sol_u(
                                t, 0, true_alpha, true_gamma
                            )  # sol_s(t, 0, 0, alpha, 1, gamma)

                        title_ = ["labeled"]

                    Obs, Pred = np.hstack((np.zeros(L.shape), L)), np.hstack(l)
                    if true_param_prefix is not None:
                        true_p = np.hstack(true_l)

                    row_ind = int(
                        np.floor(i / ncols)
                    )  # make sure unlabled and labeled are in the same column.

                    col_loc = (row_ind * sub_plot_n) * ncols * grp_len + \
                              (i % ncols - 1) * grp_len + 1
                    row_i, col_i = np.where(fig_mat == col_loc)
                    ax = plt.subplot(
                        gs[col_loc]
                    ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                    if true_param_prefix is not None:
                        if has_splicing:
                            ax.text(
                                0.05,
                                0.90,
                                r"$\alpha$ "
                                + ": {0:.2f}; ".format(true_alpha)
                                + r"$\hat \alpha$"
                                + ": {0:.2f} \n".format(alpha)
                                + r"$\beta$"
                                + ": {0:.2f}; ".format(true_beta)
                                + r"$\hat \beta$"
                                + ": {0:.2f} \n".format(beta)
                                + r"$\gamma$"
                                + ": {0:.2f}; ".format(true_gamma)
                                + r"$\hat \gamma$"
                                + ": {0:.2f} \n".format(gamma),
                                ha="left",
                                va="top",
                                transform=ax.transAxes,
                            )
                        else:
                            ax.text(
                                0.05,
                                0.90,
                                r"$\alpha$"
                                + ": {0:.2f}; ".format(true_alpha)
                                + r"$\hat \alpha$"
                                + ": {0:.2f} \n".format(alpha)
                                + r"$\gamma$"
                                + ": {0:.2f}; ".format(true_gamma)
                                + r"$\hat \gamma$"
                                + ": {0:.2f} \n".format(gamma),
                                ha="left",
                                va="top",
                                transform=ax.transAxes,
                            )
                    ax.boxplot(
                        x=[
                            Obs[np.hstack((np.zeros_like(T), T)) == std]
                            for std in [0, T_uniq[0]]
                        ],
                        positions=[0, T_uniq[0]],
                        widths=boxwidth,
                        showfliers=False,
                        showmeans=True,
                    )
                    ax.plot(t, Pred, "k--")
                    if true_param_prefix is not None:
                        ax.plot(t, true_p, "r--")
                    ax.set_xlabel("time (" + unit + ")")
                    if y_log_scale:
                        ax.set_yscale("log")
                    if log_unnormalized:
                        ax.set_ylabel("Expression (log)")
                    else:
                        ax.set_ylabel("Expression")
                    ax.set_title(gene_name + " " + title_[0])
                elif experiment_type == "mix_std_stm":
                    if has_splicing:
                        layers = (
                            ["X_uu", "X_ul", "X_su", "X_sl"]
                            if "X_ul" in valid_adata.layers.keys()
                            else ["uu", "ul", "su", "sl"]
                        )
                        uu, ul, su, sl = (
                            valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[1]],
                            valid_adata[:, gene_name].layers[layers[2]],
                            valid_adata[:, gene_name].layers[layers[3]],
                        )
                        uu, ul, su, sl = (
                            (
                                uu.toarray().squeeze(),
                                ul.toarray().squeeze(),
                                su.toarray().squeeze(),
                                sl.toarray().squeeze(),
                            )
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                        )

                        if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                            uu, ul, su, sl = (
                                np.log(uu + 1),
                                np.log(ul + 1),
                                np.log(su + 1),
                                np.log(sl + 1),
                            )

                        beta, gamma, alpha_std = valid_adata.var.loc[
                            gene_name,
                            [prefix + "beta", prefix + "gamma", prefix + "alpha_std"],
                        ]
                        alpha_stm = valid_adata[:, gene_name].varm[prefix + "alpha"].flatten()[1:]
                        alpha_stm0, k, _ = solve_first_order_deg(T_uniq[1:], alpha_stm)

                        # $u$ - unlabeled, unspliced
                        # $s$ - unlabeled, spliced
                        # $w$ - labeled, unspliced
                        # $l$ - labeled, spliced
                        #
                        # calculate labeled unspliced and spliced mRNA amount
                        u1, s1, u1_, s1_ = (
                            np.zeros(len(t) - 1),
                            np.zeros(len(t) - 1),
                            np.zeros(len(T_uniq) - 1),
                            np.zeros(len(T_uniq) - 1),
                        )
                        for ind in np.arange(1, len(t)):
                            t_i = t[ind]
                            u0 = sol_u(np.max(t) - t_i, 0, alpha_std, beta)
                            alpha_stm_t_i = alpha_stm0 * np.exp(-k * t_i)
                            u1[ind - 1], s1[ind - 1] = (
                                sol_u(t_i, u0, alpha_stm_t_i, beta),
                                sol_u(np.max(t), 0, beta, gamma),
                            )
                        for ind in np.arange(1, len(T_uniq)):
                            t_i = T_uniq[ind]
                            u0 = sol_u(np.max(T_uniq) - t_i, 0, alpha_std, beta)
                            alpha_stm_t_i = alpha_stm[ind - 1]
                            u1_[ind - 1], s1_[ind - 1] = (
                                sol_u(t_i, u0, alpha_stm_t_i, beta),
                                sol_u(np.max(T_uniq), 0, beta, gamma),
                            )

                        Obs, Pred, Pred_ = (
                            np.vstack((ul, sl, uu, su)),
                            np.vstack((u1.reshape(1, -1), s1.reshape(1, -1))),
                            np.vstack((u1_.reshape(1, -1), s1_.reshape(1, -1))),
                        )
                        j_species, title_ = (
                            4,
                            [
                                "unspliced labeled (new)",
                                "spliced labeled (new)",
                                "unspliced unlabeled (old)",
                                "spliced unlabeled (old)",
                                "alpha (steady state vs. stimulation)",
                            ],
                        )
                    else:
                        layers = (
                            ["X_new", "X_total"]
                            if "X_new" in valid_adata.layers.keys()
                            else ["new", "total"]
                        )
                        uu, ul = (
                            valid_adata[:, gene_name].layers[layers[1]]
                            - valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[0]],
                        )
                        uu, ul = (
                            (uu.toarray().squeeze(), ul.toarray().squeeze())
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze())
                        )

                        if log_unnormalized and layers == ["new", "total"]:
                            uu, ul = np.log(uu + 1), np.log(ul + 1)

                        gamma, alpha_std = valid_adata.var.loc[
                            gene_name, [prefix + "gamma", prefix + "alpha_std"]
                        ]
                        alpha_stm = valid_adata[:, gene_name].varm[prefix + "alpha"].flatten()[1:]

                        alpha_stm0, k, _ = solve_first_order_deg(T_uniq[1:], alpha_stm)
                        # require no beta functions
                        u1, u1_ = (
                            np.zeros(len(t) - 1),
                            np.zeros(len(T_uniq) - 1),
                        )  # interpolation or original time point
                        for ind in np.arange(1, len(t)):
                            t_i = t[ind]
                            u0 = sol_u(np.max(t) - t_i, 0, alpha_std, gamma)
                            alpha_stm_t_i = alpha_stm0 * np.exp(-k * t_i)
                            u1[ind - 1] = sol_u(t_i, u0, alpha_stm_t_i, gamma)

                        for ind in np.arange(1, len(T_uniq)):
                            t_i = T_uniq[ind]
                            u0 = sol_u(np.max(T_uniq) - t_i, 0, alpha_std, gamma)
                            alpha_stm_t_i = alpha_stm[ind - 1]
                            u1_[ind - 1] = sol_u(t_i, u0, alpha_stm_t_i, gamma)

                        Obs, Pred, Pred_ = (
                            np.vstack((ul, uu)),
                            np.vstack((u1.reshape(1, -1))),
                            np.vstack((u1_.reshape(1, -1))),
                        )
                        j_species, title_ = (
                            2,
                            [
                                "labeled (new)",
                                "unlabeled (old)",
                                "alpha (steady state vs. stimulation)",
                            ],
                        )

                    group_list = [np.repeat(alpha_std, len(alpha_stm)), alpha_stm]

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure all related plots for the same gene in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if j < j_species / 2:
                            ax.boxplot(
                                x=[Obs[j][T == std] for std in T_uniq[1:]],
                                positions=T_uniq[1:],
                                widths=boxwidth,
                                showfliers=False,
                                showmeans=True,
                            )
                            ax.plot(t[1:], Pred[j], "k--")
                            ax.scatter(T_uniq[1:], Pred_[j], s=20, c="red")
                            ax.set_xlabel("time (" + unit + ")")
                            ax.set_title(gene_name + ": " + title_[j])

                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Expression (log)")
                            else:
                                ax.set_ylabel("Expression")
                        elif j < j_species:
                            ax.boxplot(
                                x=[Obs[j][T == std] for std in T_uniq],
                                positions=T_uniq,
                                widths=boxwidth,
                                showfliers=False,
                                showmeans=True,
                            )
                            ax.set_xlabel("time (" + unit + ")")
                            ax.set_title(gene_name + ": " + title_[j])

                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Expression (log)")
                            else:
                                ax.set_ylabel("Expression")

                        else:
                            x = T_uniq[1:]  # the label locations
                            group_width = barwidth / 2
                            bar_coord, group_name, group_ind = (
                                [-1, 1],
                                ["steady state", "stimulation"],
                                0,
                            )

                            for group_ind in range(len(group_list)):
                                cur_group = group_list[group_ind]
                                ax.bar(
                                    x + bar_coord[group_ind] * group_width,
                                    cur_group,
                                    barwidth,
                                    label=group_name[group_ind],
                                )
                                # Add gene name, experimental type, etc.
                                ax.set_xlabel("time (" + unit + ")")
                                ax.set_ylabel("alpha (translation rate)")
                                ax.set_xticks(x)
                                ax.set_xticklabels(x)
                                group_ind += 1
                            ax.legend()

                        ax.set_xlabel("time (" + unit + ")")
                        ax.set_title(gene_name + ": " + title_[j])
                elif experiment_type == "multi_time_series":
                    pass  # group by different groups
                elif experiment_type == "coassay":
                    pass  # show protein velocity (steady state and the Gamma distribution model)
    g.autofmt_xdate(rotation=-30, ha='right')
    if save_show_or_return == "save":
        s_kwargs = {"path": None, "prefix": 'dynamics', "dpi": None,
                    "ext": 'pdf', "transparent": True, "close": True, "verbose": True}
        s_kwargs = update_dict(s_kwargs, save_kwargs)
        save_fig(**s_kwargs)
    elif save_show_or_return == "show":
        plt.tight_layout()
        plt.show()
    elif save_show_or_return == "return":
        return g


def dynamics_(
        adata,
        gene_names,
        color,
        dims=[0, 1],
        current_layer="spliced",
        use_raw=False,
        Vkey="S",
        Ekey="spliced",
        basis="umap",
        mode="all",
        cmap=None,
        gs=None,
        **kwargs
):
    """

    Parameters
    ----------
    adata
    basis
    mode: `str` (default: all)
        Support mode includes: phase, expression, velocity, all

    Returns
    -------

    """

    import matplotlib.pyplot as plt

    genes = list(set(gene_names).intersection(adata.var.index))
    for i, gn in enumerate(genes):
        ax = plt.subplot(gs[i * 3])
        try:
            ix = np.where(adata.var["Gene"] == gn)[0][0]
        except:
            continue

        scatters(
            adata,
            gene_names,
            color,
            dims=[0, 1],
            current_layer="spliced",
            use_raw=False,
            Vkey="S",
            Ekey="spliced",
            basis="umap",
            mode="all",
            cmap=None,
            gs=None,
            **kwargs
        )

        scatters(
            adata,
            gene_names,
            color,
            dims=[0, 1],
            current_layer="spliced",
            use_raw=False,
            Vkey="S",
            Ekey="spliced",
            basis="umap",
            mode="all",
            cmap=None,
            gs=None,
            **kwargs
        )

        scatters(
            adata,
            gene_names,
            color,
            dims=[0, 1],
            current_layer="spliced",
            use_raw=False,
            Vkey="S",
            Ekey="spliced",
            basis="umap",
            mode="all",
            cmap=None,
            gs=None,
            **kwargs
        )

    plt.tight_layout()



import pandas as pd
import sys
import warnings
from .utils_dynamics import *
from .utils import despline, _matplotlib_points, _datashade_points, _select_font_color
from .utils import arrowed_spines, despline_all, deaxis_all
from .utils import quiver_autoscaler, default_quiver_args
from .utils import save_fig
from .scatters import scatters
from ..estimation.csc.velocity import sol_u, sol_s, solve_first_order_deg
from ..estimation.tsc.utils_moments import moments
from ..tools.utils import get_mapper, log1p_, update_dict, get_valid_bools, index_gene
from ..configuration import _themes


def phase_portraits(
        adata,
        genes,
        x=0,
        y=1,
        pointsize=None,
        vkey=None,
        ekey=None,
        basis="umap",
        log1p=True, 
        color='cell_cycle_phase',
        use_smoothed=True,
        highlights=None,
        discrete_continous_div_themes=None,
        discrete_continous_div_cmap=None,
        discrete_continous_div_color_key=[None, None, None],
        discrete_continous_div_color_key_cmap=None,
        figsize=(6, 4),
        ncols=None,
        legend="upper left",
        background=None,
        show_quiver=False,
        quiver_size=None,
        quiver_length=None,
        frontier=True,
        q_kwargs_dict={},
        show_arrowed_spines=None,
        save_show_or_return='show',
        save_kwargs={},
        **kwargs,
):
    """Draw the phase portrait, expression values , velocity on the low dimensional embedding.
    Note that this function allows to manually set the theme, cmap, color_key and color_key_cmap
    for the phase portrait, expression and velocity subplots. When the background is 'black',
    the default themes for each of those subplots are  ["glasbey_dark", "inferno", "div_blue_black_red"],
    respectively. When the background is 'black', the default themes are  "glasbey_white", "viridis",
    "div_blue_red".

    Parameters
    ----------
        adata: :class:`~anndata.AnnData`
            an Annodata object
        genes: `list`
            A list of gene names that are going to be visualized.
        x: `int` (default: `0`)
                The column index of the low dimensional embedding for the x-axis
        y: `int` (default: `1`)
                The column index of the low dimensional embedding for the y-axis
        pointsize: `None` or `float` (default: None)
                The scale of the point size. Actual point cell size is calculated as `2500.0 / np.sqrt(adata.shape[0]) *
                pointsize`
        vkey: `string` or None (default: None)
            Which velocity key used for visualizing the magnitude of velocity. Can be either velocity in the layers slot
            or the keys in the obsm slot.
        ekey: `str` or None (default: `None`)
            The layer of data to represent the gene expression level.
        basis: `string` (default: umap)
            Which low dimensional embedding will be used to visualize the cell.
        log1p: `bool` (default: `True`)
            Whether to log1p transform the expression so that visualization can be robust to extreme values.
        color: `string` (default: 'cell_cycle_phase')
            Which group will be used to color cells, only used for the phase portrait because the other two plots are
            colored by the velocity magnitude or the gene expression value, respectively.
        use_smoothed: `bool` (default: `True`)
            Whether to use smoothed 1/2-nd moments as gene expression for the first and third columns. This is useful
            for checking the confidence of transition genes. For example, you may see a very nice linear pattern for
            some genes with the smoothed expression but this could just be an artificially generated when the number of
            expressed cells is very low. This raises red flags for the quality of the velocity values we learned for
            those genes. And we recommend to set the higher values (for example, 10% of all cells) for `min_cell_s`,
            `min_cell_u` or `shared_count` of the `fg_kwargs` argument of the dyn.pl.receipe_monocle. Note that this is
            often related to the small single cell datasets (like plate-based scRNA-seq or scSLAM-seq/NASC-seq, etc).
        highlights: `list` (default: None)
            Which color group will be highlighted. if highligts is a list of lists - each list is relate to each color
            element.
        discrete_continous_div_themes: `list[str, str, str]` (optional, default None)
            The discrete, continous and divergent color themes to use for plotting. The description for each element in
            the list is as following.
            A small set of predefined themes are provided which have relatively good aesthetics. Available themes are:
               * 'blue'
               * 'red'
               * 'green'
               * 'inferno'
               * 'fire'
               * 'viridis'
               * 'darkblue'
               * 'darkred'
               * 'darkgreen'
        discrete_continous_div_cmap: `list[str, str, str]`  (optional, default 'Blues')
            The names of  discrete, continous and divergent matplotlib colormap to use for coloring
            or shading points. The description for each element in the list is as following. If no labels or values are
            passed this will be used for shading points according to density (largely only of relevance for very large
            datasets). If values are passed this will be used for shading according the value. Note that if theme is
            passed then this value will be overridden by the corresponding option of the theme.
        discrete_continous_div_color_key: `list[dict or array,, dict or array,, dict or array,]` (default [None, None,
            None]).
            The description for each element in the list is as following. The shape (n_categories) (optional, default
            None). A list to assign discrete, continous and divergent colors to categoricals. This can either be an
            explicit dict mapping labels to colors (as strings of form '#RRGGBB'), or an array like object providing one
            color for each distinct category being provided in ``labels``. Either way this mapping will be used to color
            points according to the label. Note that if theme is passed then this value will be overridden by the
            corresponding option of the theme.
        discrete_continous_div_color_key_cmap: `list[str, str, str]`, (optional, default 'Spectral')
            The names of discrete, continous and divergent matplotlib colormap to use for categorical coloring.
            The description for each element in the list is as following. If an explicit ``color_key`` is not given a
            color mapping for categories can be generated from the label list and selecting a matching list of colors
            from the given colormap. Note that if theme is passed then this value will be overridden by the corresponding
            option of the theme.
        figsize: `None` or `[float, float]` (default: None)
                The width and height of each panel in the figure.
        ncols: `None` or `int` (default: None)
        ncol: `None` or `int` (default: None)
                Number of columns in each facet grid.
        legend: `str` (default: `on data`)
                Where to put the legend.  Legend is drawn by seaborn with “brief” mode, numeric hue and size variables
                will be represented with a sample of evenly spaced values. By default legend is drawn on top of cells.
        show_quiver: `bool` (default: False)
            Whether to show the quiver plot. If velocity for x component (corresponds to either spliced, total RNA,
            protein, etc) or y component (corresponds to either unspliced, new RNA, protein, etc) are both calculated,
            quiver represents velocity for both components otherwise the uncalculated component (usually y component)
            will be set to be 0.
        quiver_size: `float` or None (default: None)
            The size of quiver. If None, we will use set quiver_size to be 1. Note that quiver quiver_size is used to
            calculate the head_width (10 x quiver_size), head_length (12 x quiver_size) and headaxislength (8 x
            quiver_size) of the quiver. This is done via the `default_quiver_args` function which also calculate the
            scale of the quiver (1 / quiver_length).
        quiver_length: `float` or None (default: None)
            The length of quiver. The quiver length which will be used to calculate scale of quiver. Note that befoe
            applying `default_quiver_args` velocity values are first rescaled via the quiver_autoscaler function. Scale
            of quiver indicates the nuumber of data units per arrow length unit, e.g., m/s per plot width; a smaller
            scale parameter makes the arrow longer.
        q_kwargs_dict: `dict` (default: {})
            The dictionary of the quiver arguments. The default setting of quiver argument is identical to that used in
            the cell_wise_velocity and grid_velocity.
        show_arrowed_spines: bool or None (optional, default None)
            Whether to show a pair of arrowed spines represeenting the basis of the scatter is currently using. If None,
            only the first panel in the expression / velocity plot will have the arrowed spine.
        save_show_or_return: {'show', 'save', 'return'} (default: `show`)
            Whether to save, show or return the figure.
        save_kwargs: `dict` (default: `{}`)
            A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig
            function will use the {"path": None, "prefix": 'phase_portraits', "dpi": None, "ext": 'pdf', "transparent":
            True, "close": True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly
            modify those keys according to your needs.
        **kwargs:
            Additional parameters that will be passed to plt.scatter function

    Returns
    -------
        A matplotlib plot that shows 1) the phase portrait of each category used in velocity embedding, cells' low
        dimensional embedding, colored either by 2) the gene expression level or 3) the velocity magnitude values.
    """

    import matplotlib.pyplot as plt
    from matplotlib import rcParams
    from matplotlib.colors import to_hex
    # from matplotlib.colors import DivergingNorm  # TwoSlopeNorm

    has_labeling, has_splicing = adata.uns['dynamics']['has_labeling'], adata.uns['dynamics']['has_splicing']
    if background is None:
        _background = rcParams.get("figure.facecolor")
        _background = to_hex(_background) if type(_background) is tuple else _background
    else:
        _background = background

    mapper = get_mapper(smoothed=use_smoothed)

    point_size = (
        500.0 / np.sqrt(adata.shape[0]) * 5
        if pointsize is None
        else 500.0 / np.sqrt(adata.shape[0]) * 5 * pointsize
    )
    scatter_kwargs = dict(
        alpha=0.2, s=point_size, edgecolor=None, linewidth=0
    )  # (0, 0, 0, 1)

    if kwargs is not None:
        scatter_kwargs.update(kwargs)
    div_scatter_kwargs = scatter_kwargs.copy()
    # div_scatter_kwargs.update({"norm": DivergingNorm(vcenter=0)})

    if type(genes) == str:
        genes = [genes]
    _genes = list(set(adata.var.index).intersection(genes))

    # avoid object for dtype in the gamma column https://stackoverflow.com/questions/40809503/python-numpy-typeerror-ufunc-isfinite-not-supported-for-the-input-types
    k_name = 'gamma_k' if adata.uns['dynamics']['experiment_type'] == 'one-shot' else 'gamma'

    valid_id = np.isfinite(
        np.array(adata.var.loc[_genes, k_name], dtype="float")
    ).flatten()
    genes = np.array(_genes)[valid_id].tolist()
    # idx = [adata.var.index.to_list().index(i) for i in genes]

    if len(genes) == 0:
        raise Exception(
            "adata has no genes listed in your input gene vector or "
            "velocity estimation for those genes are not performed. "
            "Please try to run dyn.tl.dynamics(adata, filter_gene_mode='no')"
            "to estimate velocity for all genes: {}".format(_genes)
        )

    if not "X_" + basis in adata.obsm.keys():
        warnings.warn("{} is not applied to adata.".format(basis))
        from ..tools.dimension_reduction import reduceDimension
        reduceDimension(adata, reduction_method=basis)

    embedding = pd.DataFrame(
        {
            basis + "_0": adata.obsm["X_" + basis][:, x],
            basis + "_1": adata.obsm["X_" + basis][:, y],
        }
    )
    embedding.columns = ["dim_1", "dim_2"]

    if has_labeling and not has_splicing:
        mode = 'labeling'
        vkey = 'T' if vkey is None else vkey
        ekey = 'M_t' if ekey is None else ekey
    elif has_splicing and not has_labeling:
        mode = 'splicing'
        vkey = 'S' if vkey is None else vkey
        ekey = 'M_s' if ekey is None else ekey
    elif has_splicing and has_labeling:
        mode = 'full'
        vkey = 'T' if vkey is None else vkey
        ekey = 'M_t' if ekey is None else ekey

    layers = list(adata.layers.keys())
    layers.extend(["X", "protein", "X_protein"])
    if ekey in layers:
        if ekey == "X":
            E_vec = (
                index_gene(adata, adata.layers[mapper['X']], genes)
                if mapper["X"] in adata.layers.keys()
                else index_gene(adata, adata.X, genes)
            )
        elif ekey in ["protein", "X_protein"]:
            E_vec = (
                index_gene(adata, adata.layers[mapper[ekey]], genes)
                if (ekey in mapper.keys()) and (mapper[ekey] in adata.obsm_keys())
                else index_gene(adata, adata.obsm[ekey], genes)
            )
        else:
            E_vec = (
                index_gene(adata, adata.layers[mapper[ekey]], genes)
                if (ekey in mapper.keys()) and (mapper[ekey] in adata.layers.keys())
                else index_gene(adata, adata.layers[ekey], genes)
            )
        
        if log1p: E_vec = log1p_(adata, E_vec)

    n_cells, n_genes = adata.shape[0], len(genes)

    color_vec = np.repeat(np.nan, n_cells)
    if color is not None:
        color_vec = adata.obs[color].to_list()

    if "velocity_" not in vkey:
        vkey = "velocity_" + vkey
    if vkey == "velocity_U":
        V_vec = index_gene(adata, adata.layers['velocity_U'], genes)
        if "velocity_P" in adata.obsm.keys():
            P_vec = index_gene(adata, adata.layers['velocity_P'], genes)
    elif vkey == "velocity_S":
        V_vec = index_gene(adata, adata.layers['velocity_S'], genes)
        if "velocity_P" in adata.obsm.keys():
            P_vec = index_gene(adata, adata.layers['velocity_P'], genes)
    elif vkey == "velocity_T":
        V_vec = index_gene(adata, adata.layers['velocity_T'], genes)
        if "velocity_P" in adata.obsm.keys():
            P_vec = index_gene(adata, adata.layers['velocity_P'], genes)
    else:
        raise Exception(
            "adata has no vkey {} in either the layers or the obsm slot".format(vkey)
        )

    E_vec, V_vec = (
        E_vec.A if issparse(E_vec) else E_vec,
        V_vec.A if issparse(V_vec) else V_vec,
    )

    if k_name in adata.var.columns:
        if (
                not ("gamma_b" in adata.var.columns)
                or all(adata.var.gamma_b.isna())
        ):
            adata.var.loc[:, "gamma_b"] = 0
        gamma, velocity_offset = (
            index_gene(adata, adata.var.loc[:, k_name].values, genes),
            index_gene(adata, adata.var.gamma_b.values, genes)
        )
        (
            gamma[~np.isfinite(list(gamma))],
            velocity_offset[~np.isfinite(list(velocity_offset))],
        ) = (0, 0)
    else:
        raise Exception(
            "adata does not seem to have velocity_gamma column. Velocity estimation is required before "
            "running this function."
        )

    if mode == "labeling":
        new_mat, tot_mat = (
            index_gene(adata, adata.layers[mapper["X_new"]], genes),
            index_gene(adata, adata.layers[mapper["X_total"]], genes)
        )

        new_mat, tot_mat = (
            (new_mat.A, tot_mat.A) if issparse(new_mat) else (new_mat, tot_mat)
        )

        vel_u, vel_s = (
            index_gene(adata, adata.layers["velocity_N"].A, genes),
            index_gene(adata, adata.layers["velocity_T"].A, genes)
        )

        df = pd.DataFrame(
            {
                "new": new_mat.flatten(),
                "total": tot_mat.flatten(),
                "gene": genes * n_cells,
                "gamma": np.tile(gamma, n_cells),
                "velocity_offset": np.tile(velocity_offset, n_cells),
                "expression": E_vec.flatten(),
                "velocity": V_vec.flatten(),
                "color": np.repeat(color_vec, n_genes),
                "vel_u": vel_u.flatten(),
                "vel_s": vel_s.flatten(),
            },
            index=range(n_cells * n_genes),
        )

    elif mode == "splicing":
        unspliced_mat, spliced_mat = (
            index_gene(adata, adata.layers[mapper["X_unspliced"]], genes),
            index_gene(adata, adata.layers[mapper["X_spliced"]], genes)
        )
                
        unspliced_mat, spliced_mat = (
            (unspliced_mat.A, spliced_mat.A)
            if issparse(unspliced_mat)
            else (unspliced_mat, spliced_mat)
        )

        vel_u, vel_s = (
            np.zeros_like(index_gene(adata, adata.layers["velocity_S"].A, genes)),
            index_gene(adata, adata.layers["velocity_S"].A, genes)
        )

        df = pd.DataFrame(
            {
                "unspliced": unspliced_mat.flatten(),
                "spliced": spliced_mat.flatten(),
                "gene": genes * n_cells,
                "gamma": np.tile(gamma, n_cells),
                "velocity_offset": np.tile(velocity_offset, n_cells),
                "expression": E_vec.flatten(),
                "velocity": V_vec.flatten(),
                "color": np.repeat(color_vec, n_genes),
                "vel_u": vel_u.flatten(),
                "vel_s": vel_s.flatten(),
            },
            index=range(n_cells * n_genes),
        )

    elif mode == "full":
        uu, ul, su, sl = (
            index_gene(adata, adata.layers[mapper["X_uu"]], genes),
            index_gene(adata, adata.layers[mapper["X_ul"]], genes),
            index_gene(adata, adata.layers[mapper["X_su"]], genes),
            index_gene(adata, adata.layers[mapper["X_sl"]], genes),
        )

        vel_u, vel_s = (
            index_gene(adata, adata.layers["velocity_U"].A, genes),
            index_gene(adata, adata.layers["velocity_S"].A, genes),
        ) if vkey == 'velocity_S' else (
            index_gene(adata, adata.layers["velocity_N"].A, genes),
            index_gene(adata, adata.layers["velocity_T"].A, genes),
        )
        if "protein" in adata.obsm.keys():
            if "delta" in adata.var.columns:
                gamma_P = adata.var.delta[genes].values
                velocity_offset_P = (
                    [0] * n_cells
                    if (
                            not ("delta_b" in adata.var.columns)
                            or adata.var.delta_b.unique() is None
                    )
                    else adata.var.delta_b[genes].values
                )
            else:
                raise Exception(
                    "adata does not seem to have velocity_gamma column. Velocity estimation is required before "
                    "running this function."
                )

            P = (
                index_gene(adata, adata.obsm[mapper["X_protein"]], genes)
                if (
                        ["X_protein"] in adata.obsm.keys()
                        or [mapper["X_protein"]] in adata.obsm.keys()
                )
                else index_gene(adata, adata.obsm["protein"], genes)
            )
            uu, ul, su, sl, P = (
                (uu.A, ul.A, su.A, sl.A, P.A) if issparse(uu) else (uu, ul, su, sl, P)
            )
            if issparse(P_vec):
                P_vec = P_vec.A

            vel_p = np.zeros_like(adata.obsm["velocity_P"][:, :])

            # df = pd.DataFrame({"uu": uu.flatten(), "ul": ul.flatten(), "su": su.flatten(), "sl": sl.flatten(), "P": P.flatten(),
            #                    'gene': genes * n_cells, 'prediction': np.tile(gamma, n_cells) * uu.flatten() +
            #                     np.tile(velocity_offset, n_cells), "velocity": genes * n_cells}, index=range(n_cells * n_genes))
            df = pd.DataFrame(
                {
                    "new": (ul + sl).flatten(),
                    "total": (uu + ul + sl + su).flatten(),
                    "S": (sl + su).flatten(),
                    "P": P.flatten(),
                    "gene": genes * n_cells,
                    "gamma": np.tile(gamma, n_cells),
                    "velocity_offset": np.tile(velocity_offset, n_cells),
                    "gamma_P": np.tile(gamma_P, n_cells),
                    "velocity_offset_P": np.tile(velocity_offset_P, n_cells),
                    "expression": E_vec.flatten(),
                    "velocity": V_vec.flatten(),
                    "velocity_protein": P_vec.flatten(),
                    "color": np.repeat(color_vec, n_genes),
                    "vel_u": vel_u.flatten(),
                    "vel_s": vel_s.flatten(),
                    "vel_p": vel_p.flatten(),
                },
                index=range(n_cells * n_genes),
            )
        else:
            df = pd.DataFrame(
                {
                    "new": (ul + sl).flatten(),
                    "total": (uu + ul + sl + su).flatten(),
                    "gene": genes * n_cells,
                    "gamma": np.tile(gamma, n_cells),
                    "velocity_offset": np.tile(velocity_offset, n_cells),
                    "expression": E_vec.flatten(),
                    "velocity": V_vec.flatten(),
                    "color": np.repeat(color_vec, n_genes),
                    "vel_u": vel_u.flatten(),
                    "vel_s": vel_s.flatten(),
                },
                index=range(n_cells * n_genes),
            )
    else:
        raise Exception(
            "Your adata is corrupted. Make sure that your layer has keys new, old for the labelling mode, "
            "spliced, ambiguous, unspliced for the splicing model and uu, ul, su, sl for the full mode"
        )

    num_per_gene = 6 if ("protein" in adata.obsm.keys() and mode == "full") else 3
    ncols = min([num_per_gene, ncols]) if ncols is not None else num_per_gene
    nrow, ncol = int(np.ceil(num_per_gene * n_genes / ncols)), ncols
    if figsize is None:
        g = plt.figure(None, (3 * ncol, 3 * nrow), facecolor=_background)  # , dpi=160
    else:
        g = plt.figure(None, (figsize[0] * ncol, figsize[1] * nrow), facecolor=_background)  # , dpi=160

    if discrete_continous_div_themes is None:
        if _background in ["#ffffff", "black"]:
            discrete_theme, continous_theme, divergent_theme = (
                "glasbey_dark",
                "inferno",
                "div_blue_black_red",
            )
        else:
            discrete_theme, continous_theme, divergent_theme = (
                "glasbey_white",
                "viridis",
                "div_blue_red",
            )
    else:
        discrete_theme, continous_theme, divergent_theme = discrete_continous_div_themes

    discrete_cmap, discrete_color_key_cmap, discrete_background = (
        _themes[discrete_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[0],
        _themes[discrete_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
        discrete_continous_div_color_key_cmap[0],
        _themes[discrete_theme]["background"],
    )
    continous_cmap, continous_color_key_cmap, continous_background = (
        _themes[continous_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[1],
        _themes[continous_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
        discrete_continous_div_color_key_cmap[1],
        _themes[continous_theme]["background"],
    )
    divergent_cmap, divergent_color_key_cmap, divergent_background = (
        _themes[divergent_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[2],
        _themes[divergent_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
        discrete_continous_div_color_key_cmap[2],
        _themes[divergent_theme]["background"],
    )

    font_color = _select_font_color(discrete_background)

    # the following code is inspired by https://github.com/velocyto-team/velocyto-notebooks/blob/master/python/DentateGyrus.ipynb
    gs = plt.GridSpec(nrow, ncol, wspace=0.5, hspace=0.36)
    for i, gn in enumerate(genes):
        if num_per_gene == 3:
            ax1, ax2, ax3 = (
                plt.subplot(gs[i * 3]),
                plt.subplot(gs[i * 3 + 1]),
                plt.subplot(gs[i * 3 + 2]),
            )
        elif num_per_gene == 6:
            ax1, ax2, ax3, ax4, ax5, ax6 = (
                plt.subplot(gs[i * 3]),
                plt.subplot(gs[i * 3 + 1]),
                plt.subplot(gs[i * 3 + 2]),
                plt.subplot(gs[i * 3 + 3]),
                plt.subplot(gs[i * 3 + 4]),
                plt.subplot(gs[i * 3 + 5]),
            )
        try:
            ix = np.where(adata.var.index == gn)[0][0]
        except:
            continue
        cur_pd = df.loc[df.gene == gn, :]
        if cur_pd.color.isna().all():
            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax1, color = _matplotlib_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=None,
                    values=cur_pd.loc[:, "expression"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
            else:
                ax1, color = _datashade_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=None,
                    values=cur_pd.loc[:, "expression"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
        else:
            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax1, color = _matplotlib_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=cur_pd.loc[:, "color"],
                    values=None,
                    highlights=highlights,
                    cmap=discrete_cmap,
                    color_key=discrete_continous_div_color_key[0],
                    color_key_cmap=discrete_color_key_cmap,
                    background=discrete_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
            else:
                ax1, color = _datashade_points(
                    cur_pd.iloc[:, [1, 0]].values,
                    ax=ax1,
                    labels=cur_pd.loc[:, "color"],
                    values=None,
                    highlights=highlights,
                    cmap=discrete_cmap,
                    color_key=discrete_continous_div_color_key[0],
                    color_key_cmap=discrete_color_key_cmap,
                    background=discrete_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )

        ax1.set_title(gn)
        ax1.set_xlabel("spliced (1st moment)")
        ax1.set_ylabel("unspliced (1st moment)")
        xnew = np.linspace(0, cur_pd.iloc[:, 1].max() * 0.80)
        ax1.plot(
            xnew,
            xnew * cur_pd.loc[:, "gamma"].unique()
            + cur_pd.loc[:, "velocity_offset"].unique(),
            dashes=[6, 2],
            c=font_color,
        )
        X_array, V_array = (
            cur_pd.iloc[:, [1, 0]].values,
            cur_pd.loc[:, ["vel_s", "vel_u"]].values,
        )

        # add quiver:
        if show_quiver:
            V_array /= 3 * quiver_autoscaler(X_array, V_array)

            if background is None:
                background = rcParams.get("figure.facecolor")
            if quiver_size is None:
                quiver_size = 1
            if background == "black":
                edgecolors = "white"
            else:
                edgecolors = "black"

            head_w, head_l, ax_l, scale = default_quiver_args(
                quiver_size, quiver_length
            )

            quiver_kwargs = {
                "angles": "xy",
                "scale": scale,
                "scale_units": "xy",
                "width": 0.0005,
                "headwidth": head_w,
                "headlength": head_l,
                "headaxislength": ax_l,
                "minshaft": 1,
                "minlength": 1,
                "pivot": "tail",
                "edgecolors": edgecolors,
                "linewidth": 0.2,
                "color": color,
                "alpha": 1,
                "zorder": 10,
            }
            quiver_kwargs = update_dict(quiver_kwargs, q_kwargs_dict)
            ax1.quiver(
                X_array[:, 0],
                X_array[:, 1],
                V_array[:, 0],
                V_array[:, 1],
                **quiver_kwargs
            )

        ax1.set_xlim(0, np.max(X_array[:, 0]) * 1.02)
        ax1.set_ylim(0, np.max(X_array[:, 1]) * 1.02)

        despline(ax1)  # sns.despline()

        df_embedding = pd.concat([cur_pd, embedding], axis=1)
        V_vec = cur_pd.loc[:, "velocity"]

        # limit = np.nanmax(
        #     np.abs(np.nanpercentile(V_vec, [1, 99]))
        # )  # upper and lowe limit / saturation
        #
        # V_vec = V_vec + limit  # that is: tmp_colorandum - (-limit)
        # V_vec = V_vec / (2 * limit)  # that is: tmp_colorandum / (limit - (-limit))
        # V_vec = np.clip(V_vec, 0, 1)
        if show_arrowed_spines is None:
            show_arrowed_spines_ = True if i == 0 else False
        else:
            show_arrowed_spines_ = show_arrowed_spines

        if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
            ax2, _ = _matplotlib_points(
                embedding.iloc[:, :2].values,
                ax=ax2,
                labels=None,
                values=cur_pd.loc[:, "expression"].values,
                highlights=highlights,
                cmap=continous_cmap,
                color_key=discrete_continous_div_color_key[1],
                color_key_cmap=continous_color_key_cmap,
                background=continous_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                **scatter_kwargs
            )
        else:
            ax2, _ = _datashade_points(
                embedding.iloc[:, :2].values,
                ax=ax2,
                labels=None,
                values=cur_pd.loc[:, "expression"].values,
                highlights=highlights,
                cmap=continous_cmap,
                color_key=discrete_continous_div_color_key[1],
                color_key_cmap=continous_color_key_cmap,
                background=continous_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                **scatter_kwargs
            )

        ax2.set_title(gn + " (" + ekey + ")")
        if show_arrowed_spines_:
            ax2 = arrowed_spines(ax2, basis)
        else:
            despline_all(ax2)
            deaxis_all(ax2)

        v_max = 0.01 if min(V_vec) + max(V_vec) == 0 else np.max(np.abs(V_vec.values))
        div_scatter_kwargs.update({"vmin": -v_max, "vmax": v_max})

        if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
            ax3, _ = _matplotlib_points(
                embedding.iloc[:, :2].values,
                ax=ax3,
                labels=None,
                values=V_vec.values,
                highlights=highlights,
                cmap=divergent_cmap,
                color_key=discrete_continous_div_color_key[2],
                color_key_cmap=divergent_color_key_cmap,
                background=divergent_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                sort='abs',
                frontier=frontier,
                **div_scatter_kwargs
            )
        else:
            ax3, _ = _datashade_points(
                embedding.iloc[:, :2].values,
                ax=ax3,
                labels=None,
                values=V_vec.values,
                highlights=highlights,
                cmap=divergent_cmap,
                color_key=discrete_continous_div_color_key[2],
                color_key_cmap=divergent_color_key_cmap,
                background=divergent_background,
                width=figsize[0],
                height=figsize[1],
                show_legend=legend,
                sort='abs',
                frontier=frontier,
                **div_scatter_kwargs
            )

        ax3.set_title(gn + " (" + vkey + ")")
        if show_arrowed_spines_:
            ax3 = arrowed_spines(ax3, basis)
        else:
            despline_all(ax3)
            deaxis_all(ax3)

        if (
                "protein" in adata.obsm.keys()
                and mode == "full"
                and all([i in adata.layers.keys() for i in ["uu", "ul", "su", "sl"]])
        ):
            if cur_pd.color.unique() != np.nan:
                if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                    ax4, color = _matplotlib_points(
                        cur_pd.iloc[:, [3, 2]].values,
                        ax=ax4,
                        labels=None,
                        values=cur_pd.loc[:, "expression"].values,
                        highlights=highlights,
                        cmap=continous_cmap,
                        color_key=discrete_continous_div_color_key[1],
                        color_key_cmap=continous_color_key_cmap,
                        background=continous_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )
                else:
                    ax4, color = _datashade_points(
                        cur_pd.iloc[:, [3, 2]].values,
                        ax=ax4,
                        labels=None,
                        values=cur_pd.loc[:, "expression"].values,
                        highlights=highlights,
                        cmap=continous_cmap,
                        color_key=discrete_continous_div_color_key[1],
                        color_key_cmap=continous_color_key_cmap,
                        background=continous_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )
            else:
                if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                    ax4, color = _matplotlib_points(
                        cur_pd.iloc[:, [1, 0]].values,
                        ax=ax4,
                        labels=cur_pd.loc[:, "color"].values,
                        values=None,
                        highlights=highlights,
                        cmap=discrete_cmap,
                        color_key=discrete_continous_div_color_key[0],
                        color_key_cmap=discrete_color_key_cmap,
                        background=discrete_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )
                else:
                    ax4, color = _datashade_points(
                        cur_pd.iloc[:, [1, 0]].values,
                        ax=ax4,
                        labels=cur_pd.loc[:, "color"].values,
                        values=None,
                        highlights=highlights,
                        cmap=discrete_cmap,
                        color_key=discrete_continous_div_color_key[0],
                        color_key_cmap=discrete_color_key_cmap,
                        background=discrete_background,
                        width=figsize[0],
                        height=figsize[1],
                        show_legend=legend,
                        **scatter_kwargs
                    )

            ax4.set_title(gn)
            ax1.set_xlabel("spliced (1st moment)")
            ax1.set_ylabel("protein (1st moment)")

            xnew = np.linspace(0, cur_pd.iloc[:, 3].max())
            ax4.plot(
                xnew,
                xnew * cur_pd.loc[:, "gamma_P"].unique()
                + cur_pd.loc[:, "velocity_offset_P"].unique(),
                dashes=[6, 2],
                c=font_color,
            )
            X_array, V_array = (
                cur_pd.iloc[:, [3, 2]].values,
                cur_pd.loc[:, ["vel_p", "vel_s"]].values,
            )

            # add quiver:
            if show_quiver:
                V_array /= 3 * quiver_autoscaler(X_array, V_array)

                if background is None:
                    background = rcParams.get("figure.facecolor")
                if quiver_size is None:
                    quiver_size = 1
                if background == "black":
                    edgecolors = "white"
                else:
                    edgecolors = "black"

                head_w, head_l, ax_l, scale = default_quiver_args(
                    quiver_size, quiver_length
                )

                quiver_kwargs = {
                    "angles": "xy",
                    "scale": scale,
                    "scale_units": "xy",
                    "width": 0.0005,
                    "headwidth": head_w,
                    "headlength": head_l,
                    "headaxislength": ax_l,
                    "minshaft": 1,
                    "minlength": 1,
                    "pivot": "tail",
                    "edgecolors": edgecolors,
                    "linewidth": 0.2,
                    "color": color,
                    "alpha": 1,
                    "zorder": 10,
                }
                quiver_kwargs = update_dict(quiver_kwargs, q_kwargs_dict)
                ax4.quiver(
                    X_array[:, 0],
                    X_array[:, 1],
                    V_array[:, 0],
                    V_array[:, 1],
                    **quiver_kwargs
                )

            ax4.set_ylim(0, np.max(X_array[:, 0]) * 1.02)
            ax4.set_xlim(0, np.max(X_array[:, 1]) * 1.02)

            despline(ax1)  # sns.despline()

            V_vec = df_embedding.loc[:, "velocity_p"]

            limit = np.nanmax(
                np.abs(np.nanpercentile(V_vec, [1, 99]))
            )  # upper and lowe limit / saturation

            V_vec = V_vec + limit  # that is: tmp_colorandum - (-limit)
            V_vec = V_vec / (2 * limit)  # that is: tmp_colorandum / (limit - (-limit))
            V_vec = np.clip(V_vec, 0, 1)

            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax5, _ = _matplotlib_points(
                    embedding.iloc[:, :2],
                    ax=ax5,
                    labels=None,
                    values=embedding.loc[:, "P"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )
            else:
                ax5, _ = _datashade_points(
                    embedding.iloc[:, :2],
                    ax=ax5,
                    labels=None,
                    values=embedding.loc[:, "P"].values,
                    highlights=highlights,
                    cmap=continous_cmap,
                    color_key=discrete_continous_div_color_key[1],
                    color_key_cmap=continous_color_key_cmap,
                    background=continous_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    **scatter_kwargs
                )

            ax5.set_title(gn + " (protein expression)")
            if show_arrowed_spines_:
                ax5 = arrowed_spines(ax5, basis)
            else:
                despline_all(ax5)
                deaxis_all(ax5)

            v_max = np.max(np.abs(V_vec.values))
            div_scatter_kwargs.update({"vmin": -v_max, "vmax": v_max})

            if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
                ax6, _ = _matplotlib_points(
                    embedding.iloc[:, :2],
                    ax=ax6,
                    labels=None,
                    values=V_vec.values,
                    highlights=highlights,
                    cmap=divergent_cmap,
                    color_key=discrete_continous_div_color_key[2],
                    color_key_cmap=divergent_color_key_cmap,
                    background=divergent_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    sort='abs',
                    frontier=frontier,
                    **div_scatter_kwargs
                )
            else:
                ax6, _ = _datashade_points(
                    embedding.iloc[:, :2],
                    ax=ax6,
                    labels=None,
                    values=V_vec.values,
                    highlights=highlights,
                    cmap=divergent_cmap,
                    color_key=discrete_continous_div_color_key[2],
                    color_key_cmap=divergent_color_key_cmap,
                    background=divergent_background,
                    width=figsize[0],
                    height=figsize[1],
                    show_legend=legend,
                    sort='abs',
                    frontier=frontier,
                    **div_scatter_kwargs
                )

            ax6.set_title(gn + " (protein velocity)")
            if show_arrowed_spines_:
                ax6 = arrowed_spines(ax6, basis)
            else:
                despline_all(ax6)
                deaxis_all(ax6)

    if save_show_or_return == "save":
        s_kwargs = {"path": None, "prefix": 'phase_portraits', "dpi": None,
                    "ext": 'pdf', "transparent": True, "close": True, "verbose": True}
        s_kwargs = update_dict(s_kwargs, save_kwargs)

        save_fig(**s_kwargs)
    elif save_show_or_return == "show":
        
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            plt.tight_layout()
        
        plt.show()
    elif save_show_or_return == "return":
        return g


def dynamics(
        adata,
        genes,
        unit="hours",
        log_unnormalized=True,
        y_log_scale=False,
        ci=None,
        ncols=None,
        figsize=None,
        dpi=None,
        boxwidth=None,
        barwidth=None,
        true_param_prefix=None,
        show_moms_fit=False,
        show_variance=True,
        show_kin_parameters=True,
        gene_order='column',
        font_size_scale=1,
        save_show_or_return='show',
        save_kwargs={},
):
    """Plot the data and fitting of different metabolic labeling experiments.
    Note that if non-smoothed data was used for kinetic fitting, you often won't see boxplot
    but only the triangles for the mean values. This is because the raw data has a lot dropout,
    thus the median is outside of the whiskers of the boxplot and the boxplot is then not drawn
    by default.

    Parameters
    ----------
        adata: :class:`~anndata.AnnData`
            an Annodata object.
        genes: list of `str`
            key for variable or gene names.
        unit: `str` (default: `hour`)
            The unit of the labeling time, for example, `hours` or `minutes`.
        y_log_scale: `bool` (default: `True`)
            Whether or not to use log scale for y-axis.
        ci: `str` (for example: "95%") or None (default: `None`)
            The confidence interval to be drawed for the parameter fitting. Currently not used.
        ncols: `int` or None (default: `None`)
            The number of columns in the plot.
        figsize: `[float, float]` or `(float, float)` or None
            The size of the each panel in the figure.
        dpi: `float` or None
            Figure resolution.
        boxwidth: `float`
            The width of the box of the boxplot.
        barwidth: `float`
            The width of the bar of the barplot.
        true_param_prefix: `str`
            The prefix for the column names of true parameters in the .var attributes. Useful for the simulation data.
        show_moms_fit: `bool` (default: `True`)
            Whether to show fitting curves associated with the stochastic models, only works for non-deterministic models.
        show_variance: `bool` (default: `True`)
            Whether to add a boxplot to show the variance at each time point.
        show_kin_parameters: `bool` (default: `True`)
            Whether to include the estimated kinetic parameter values on the plot.
        gene_order: `str` (default: `column`)
            The order of genes to present on the figure, either row-major or column major.
        font_size_scale: `float` (default: `1`)
            A value that will be used for scaling
        save_show_or_return: {'show', 'save', 'return'} (default: `show`)
            Whether to save, show or return the figure.
        save_kwargs: `dict` (default: `{}`)
            A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function
            will use the {"path": None, "prefix": 'dynamics', "dpi": None, "ext": 'pdf', "transparent": True, "close":
            True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys
            according to your needs.

    Returns
    -------
        Nothing but plot the figure of the metabolic fitting.
    """

    import matplotlib.pyplot as plt
    import matplotlib
    params = {'font.size': 4 * font_size_scale,
              'legend.fontsize': 4 * font_size_scale,
              'legend.handlelength': 0.5,
              'axes.labelsize': 6 * font_size_scale,
              'axes.titlesize': 6 * font_size_scale,
              'xtick.labelsize': 6 * font_size_scale,
              'ytick.labelsize': 6 * font_size_scale,
              'axes.titlepad': 1,
              'axes.labelpad': 1,
              'axes.spines.right': False,
              'axes.spines.top': False
              }
    matplotlib.rcParams.update(params)

    show_kin_parameters = True if true_param_prefix else show_kin_parameters

    uns_keys = np.array(adata.uns_keys())
    tmp = np.array([i.split("_dynamics")[0] if i.endswith('_dynamics') else None for i in uns_keys])
    tmp1 = [False if i is None else True for i in tmp]
    group = tmp[tmp1][0] if sum(tmp1) > 0 else None

    if group is not None:
        uns_key = group + "_dynamics"
        _group, grp_len = np.unique(adata.obs[group]), len(np.unique(adata.obs[group]))
    else:
        uns_key = "dynamics"
        _group, grp_len = ["_all_cells"], 1

    experiment_type = adata.uns[uns_key]['experiment_type']
    T = adata.uns[uns_key]['t']
    filter_gene_mode = adata.uns[uns_key]['filter_gene_mode']
    X_data = adata.uns[uns_key]['X_data']
    X_fit_data = adata.uns[uns_key]['X_fit_data']
    has_splicing = adata.uns[uns_key]['has_splicing']
    model = adata.uns[uns_key]['model']
    use_smoothed = adata.uns[uns_key]['use_smoothed']

    # for key, val in adata.uns[uns_key].items():
    #     exec(key + '=val')

    if experiment_type == "conventional":
        # run the phase_portraits plot
        warnings.warn(
            "dynamics plot doesn't support conventional experiment type, using phase_portraits function instead."
        )
        phase_portraits(adata)

    T_uniq = np.unique(T)
    t = np.linspace(0, T_uniq[-1], 1000)
    valid_genes = adata.var_names[get_valid_bools(adata, filter_gene_mode)]
    valid_gene_names = valid_genes.intersection(genes)

    if len(valid_gene_names) == 0:
        raise ValueError(f"no kinetic parameter fitting has performed for the gene {genes} you provided. Try "
                         f"using dyn.tl.dynamics(adata, filter_gene_mode='final', ...) or use only fitted genes.")

    valid_adata = adata[:, valid_gene_names]
    gene_idx = np.array([np.where(valid_genes == gene)[0][0] for gene in valid_gene_names])
    X_data, X_fit_data = [X_data[i] for i in gene_idx], [X_fit_data[i] for i in gene_idx]

    if boxwidth is None and len(T_uniq) > 1:
        boxwidth = 0.8 * (np.diff(T_uniq).min() / 2)

    if barwidth is None and len(T_uniq) > 1:
        barwidth = 0.8 * (np.diff(T_uniq).min() / 2)

    if has_splicing:
        if model == "moment":
            sub_plot_n = 4  # number of subplots for each gene
        elif experiment_type == "kin":
            if model == 'deterministic':
                sub_plot_n = 2 if show_variance else 1
            elif model == 'stochastic':
                mom_n = 3 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
            elif model == 'mixture':
                sub_plot_n = 4 if show_variance else 2
            elif model == 'mixture_deterministic_stochastic':
                mom_n = 3 if show_moms_fit else 0
                sub_plot_n = 4 + mom_n if show_variance else 2 + mom_n
            elif model == 'mixture_stochastic_stochastic':
                mom_n = 6 if show_moms_fit else 0
                sub_plot_n = 4 + mom_n if show_variance else 2 + mom_n
        elif experiment_type == "deg":
            if model == 'deterministic':
                sub_plot_n = 2 if show_variance else 1
            elif model == 'stochastic':
                mom_n = 3 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
        elif experiment_type == "one_shot":  # just the labeled RNA
            sub_plot_n = 1
        elif experiment_type == "mix_std_stm":
            sub_plot_n = 5
    else:
        if model == "moment":
            sub_plot_n = 2  # number of subplots for each gene
        elif experiment_type == "kin":
            if model == 'deterministic':
                sub_plot_n = 1
            elif model == 'stochastic':
                mom_n = 1 if show_moms_fit else 0
                sub_plot_n = 1 + mom_n if show_variance else 1 + mom_n
            elif model == 'mixture':
                sub_plot_n = 2 if show_variance else 1
            elif model == 'mixture_deterministic_stochastic':
                mom_n = 1 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
            elif model == 'mixture_stochastic_stochastic':
                mom_n = 2 if show_moms_fit else 0
                sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
        elif experiment_type == "deg":
            if model == 'deterministic':
                sub_plot_n = 1
            elif model == 'stochastic':
                mom_n = 1 if show_moms_fit else 0
                sub_plot_n = 1 + mom_n
        elif experiment_type == "one_shot":  # just the labeled RNA
            sub_plot_n = 1
        elif experiment_type == "mix_std_stm":
            sub_plot_n = 3

    ncols = ( # each column correspond to one gene
        len(gene_idx) * grp_len
        if ncols is None
        else min(len(gene_idx) * grp_len, ncols)
    )
    nrows = int(np.ceil(len(gene_idx) * sub_plot_n * grp_len / ncols))
    figsize = [7, 5] if figsize is None else figsize
    g = plt.figure(None, (figsize[0] * ncols, figsize[1] * nrows), dpi=dpi)
    gs = plt.GridSpec(
        nrows,
        ncols,
        g,
        wspace=0.12,
    )

    # there are bugs when fig is not a symmetric matrix
    fig_mat = np.arange(nrows * ncols).reshape((nrows, ncols))

    # we need to visualize gene in row-wise mode
    for grp_idx, cur_grp in enumerate(_group):
        if cur_grp == "_all_cells":
            prefix = ""  # kinetic_parameter_
        else:
            prefix = group + "_" + cur_grp + "_"

        true_p = None;
        true_params = [None, None, None]
        logLL = valid_adata.var.loc[valid_gene_names, prefix + 'logLL']
        est_params_df = valid_adata.var.loc[
            valid_gene_names,
            [
                prefix + "alpha",
                prefix + "beta",
                prefix + "gamma",
                "half_life",
            ],
        ]

        est_params = [est_params_df.loc[:, 'alpha'].values, est_params_df.loc[:, 'beta'].values,
                      est_params_df.loc[:, 'gamma'].values]

        if experiment_type == "kin":
            if model == 'deterministic':
                gs = plot_kin_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_variance, show_kin_parameters, )
            elif model == 'stochastic':
                gs = plot_kin_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
            elif model == 'mixture':
                gs = plot_kin_mix(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_variance, show_kin_parameters, )
            elif model == 'mixture_deterministic_stochastic':
                gs = plot_kin_mix_det_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
            elif model == 'mixture_stochastic_stochastic':
                gs = plot_kin_mix_sto_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
        elif experiment_type == "deg":
            if model == 'deterministic':
                gs = plot_deg_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_variance, show_kin_parameters, )
            elif model == 'stochastic':
                gs = plot_deg_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                  t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                  grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                  true_param_prefix, true_params, est_params,
                                  show_moms_fit, show_variance, show_kin_parameters, )
        else:
            for i, gene_name in enumerate(valid_genes):
                if model == "moment":
                    a, b, alpha_a, alpha_i, beta, gamma = valid_adata.var.loc[
                        gene_name,
                        [
                            prefix + "a",
                            prefix + "b",
                            prefix + "alpha_a",
                            prefix + "alpha_i",
                            prefix + "beta",
                            prefix + "gamma",
                        ],
                    ]
                    params = {
                        "a": a,
                        "b": b,
                        "alpha_a": alpha_a,
                        "alpha_i": alpha_i,
                        "beta": beta,
                        "gamma": gamma,
                    }  # "la": 1, "si": 0,
                    mom = moments(*list(params.values()))
                    mom.integrate(t)
                    mom_data = (
                        mom.get_all_central_moments()
                        if has_splicing
                        else mom.get_nosplice_central_moments()
                    )
                    if true_param_prefix is not None:
                        (
                            true_a,
                            true_b,
                            true_alpha_a,
                            true_alpha_i,
                            true_beta,
                            true_gamma,
                        ) = (
                            valid_adata.var.loc[gene_name, true_param_prefix + "a"]
                            if true_param_prefix + "a" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "b"]
                            if true_param_prefix + "b" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "alpha_a"]
                            if true_param_prefix + "alpha_a" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "alpha_i"]
                            if true_param_prefix + "alpha_i" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                            if true_param_prefix + "beta" in valid_adata.var_keys()
                            else -np.inf,
                            valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                            if true_param_prefix + "gamma" in valid_adata.var_keys()
                            else -np.inf,
                        )

                        true_params = {
                            "a": true_a,
                            "b": true_b,
                            "alpha_a": true_alpha_a,
                            "alpha_i": true_alpha_i,
                            "beta": true_beta,
                            "gamma": true_gamma,
                        }  # "la": 1, "si": 0,
                        true_mom = moments(*list(true_params.values()))
                        true_mom.integrate(t)
                        true_mom_data = (
                            true_mom.get_all_central_moments()
                            if has_splicing
                            else true_mom.get_nosplice_central_moments()
                        )

                    # n_mean, n_var = x_data[:2, :], x_data[2:, :]
                    if has_splicing:
                        tmp = (
                            [
                                valid_adata[:, gene_name].layers["M_ul"].A.T,
                                valid_adata.layers["M_sl"].A.T,
                            ]
                            if "M_ul" in valid_adata.layers.keys()
                            else [
                                valid_adata[:, gene_name].layers["ul"].A.T,
                                valid_adata.layers["sl"].A.T,
                            ]
                        )
                        x_data = [tmp[0].A, tmp[1].A] if issparse(tmp[0]) else tmp
                        if log_unnormalized and "X_ul" not in valid_adata.layers.keys():
                            x_data = [np.log(tmp[0] + 1), np.log(tmp[1] + 1)]

                        title_ = [
                            "(unspliced labeled)",
                            "(spliced labeled)",
                            "(unspliced labeled)",
                            "(spliced labeled)",
                        ]
                        Obs_m = [valid_adata.uns["M_ul"], valid_adata.uns["M_sl"]]
                        Obs_v = [valid_adata.uns["V_ul"], valid_adata.uns["V_sl"]]
                        j_species = 2  # number of species
                    else:
                        tmp = (
                            valid_adata[:, gene_name].layers["X_new"].T
                            if "X_new" in valid_adata.layers.keys()
                            else valid_adata[:, gene_name].layers["new"].T
                        )
                        x_data = [tmp.A] if issparse(tmp) else [tmp]

                        if log_unnormalized and "X_new" not in valid_adata.layers.keys():
                            x_data = [np.log(x_data[0] + 1)]
                        # only use new key for calculation, so we only have M, V
                        title_ = [" (labeled)", " (labeled)"]
                        Obs_m, Obs_v = [valid_adata.uns["M"]], [valid_adata.uns["V"]]
                        j_species = 1

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure all related plots for the same gene in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if true_param_prefix is not None and j == 0:
                            if has_splicing:
                                ax.text(
                                    0.05,
                                    0.92,
                                    r"$a$"
                                    + ": {0:.2f}; ".format(true_a)
                                    + r"$\hat a$"
                                    + ": {0:.2f} \n".format(a)
                                    + r"$b$"
                                    + ": {0:.2f}; ".format(true_b)
                                    + r"$\hat b$"
                                    + ": {0:.2f} \n".format(b)
                                    + r"$\alpha_a$"
                                    + ": {0:.2f}; ".format(true_alpha_a)
                                    + r"$\hat \alpha_a$"
                                    + ": {0:.2f} \n".format(alpha_a)
                                    + r"$\alpha_i$"
                                    + ": {0:.2f}; ".format(true_alpha_i)
                                    + r"$\hat \alpha_i$"
                                    + ": {0:.2f} \n".format(alpha_i)
                                    + r"$\beta$"
                                    + ": {0:.2f}; ".format(true_beta)
                                    + r"$\hat \beta$"
                                    + ": {0:.2f} \n".format(beta)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="left",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                            else:
                                ax.text(
                                    0.05,
                                    0.92,
                                    r"$a$"
                                    + ": {0:.2f}; ".format(true_a)
                                    + r"$\hat a$"
                                    + ": {0:.2f} \n".format(a)
                                    + r"$b$"
                                    + ": {0:.2f}; ".format(true_b)
                                    + r"$\hat b$"
                                    + ": {0:.2f} \n".format(b)
                                    + r"$\alpha_a$"
                                    + ": {0:.2f}; ".format(true_alpha_a)
                                    + r"$\hat \alpha_a$"
                                    + ": {0:.2f} \n".format(alpha_a)
                                    + r"$\alpha_i$"
                                    + ": {0:.2f}; ".format(true_alpha_i)
                                    + r"$\hat \alpha_i$"
                                    + ": {0:.2f} \n".format(alpha_i)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="left",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                        if j < j_species:
                            ax.boxplot(
                                x=[x_data[j][i][T == std] for std in T_uniq],
                                positions=T_uniq,
                                widths=boxwidth,
                                showfliers=False,
                                showmeans=True,
                            )  # x=T.values, y= # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10)
                            # ax.scatter(T_uniq, Obs_m[j][i], c='r')  # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10)
                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Expression (log)")  #
                            else:
                                ax.set_ylabel("Expression")
                            ax.plot(t, mom_data[j], "k--")
                            if true_param_prefix is not None:
                                ax.plot(t, true_mom_data[j], "r--")
                        else:
                            ax.scatter(T_uniq, Obs_v[j - j_species][i])  # , c='r'
                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Variance (log expression)")  #
                            else:
                                ax.set_ylabel("Variance")
                            ax.plot(t, mom_data[j], "k--")
                            if true_param_prefix is not None:
                                ax.plot(t, true_mom_data[j], "r--")

                        ax.set_xlabel("time (" + unit + ")")
                        ax.set_title(gene_name + " " + title_[j])

                elif experiment_type == "deg":
                    if has_splicing:
                        layers = (
                            ["X_uu", "X_ul", "X_su", "X_sl"]
                            if "X_ul" in valid_adata.layers.keys()
                            else ["uu", "ul", "su", "sl"]
                        )
                        uu, ul, su, sl = (
                            valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[1]],
                            valid_adata[:, gene_name].layers[layers[2]],
                            valid_adata[:, gene_name].layers[layers[3]],
                        )
                        uu, ul, su, sl = (
                            (
                                uu.toarray().squeeze(),
                                ul.toarray().squeeze(),
                                su.toarray().squeeze(),
                                sl.toarray().squeeze(),
                            )
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                        )

                        if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                            uu, ul, su, sl = (
                                np.log(uu + 1),
                                np.log(ul + 1),
                                np.log(su + 1),
                                np.log(sl + 1),
                            )

                        alpha, beta, gamma, ul0, sl0, uu0, half_life = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "beta",
                                prefix + "gamma",
                                prefix + "ul0",
                                prefix + "sl0",
                                prefix + "uu0",
                                "half_life",
                            ],
                        ]
                        # $u$ - unlabeled, unspliced
                        # $s$ - unlabeled, spliced
                        # $w$ - labeled, unspliced
                        # $l$ - labeled, spliced
                        #
                        beta = sys.float_info.epsilon if beta == 0 else beta
                        gamma = sys.float_info.epsilon if gamma == 0 else gamma
                        u = sol_u(t, uu0, alpha, beta)
                        su0 = np.mean(su[T == np.min(T)])  # this should also be estimated
                        s = sol_s(t, su0, uu0, alpha, beta, gamma)
                        w = sol_u(t, ul0, 0, beta)
                        l = sol_s(t, sl0, ul0, 0, beta, gamma)
                        if true_param_prefix is not None:
                            true_alpha, true_beta, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                                if true_param_prefix + "beta" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )

                            true_u = sol_u(t, uu0, true_alpha, true_beta)
                            true_s = sol_s(t, su0, uu0, true_alpha, true_beta, true_gamma)
                            true_w = sol_u(t, ul0, 0, true_beta)
                            true_l = sol_s(t, sl0, ul0, 0, true_beta, true_gamma)

                            true_p = np.vstack((true_u, true_w, true_s, true_l))

                        title_ = [
                            "(unspliced unlabeled)",
                            "(unspliced labeled)",
                            "(spliced unlabeled)",
                            "(spliced labeled)",
                        ]

                        Obs, Pred = np.vstack((uu, ul, su, sl)), np.vstack((u, w, s, l))
                    else:
                        layers = (
                            ["X_new", "X_total"]
                            if "X_new" in valid_adata.layers.keys()
                            else ["new", "total"]
                        )
                        uu, ul = (
                            valid_adata[:, gene_name].layers[layers[1]]
                            - valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[0]],
                        )
                        uu, ul = (
                            (uu.toarray().squeeze(), ul.toarray().squeeze())
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze())
                        )

                        if log_unnormalized and layers == ["new", "total"]:
                            uu, ul = np.log(uu + 1), np.log(ul + 1)

                        alpha, gamma, uu0, ul0, half_life = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "gamma",
                                prefix + "uu0",
                                prefix + "ul0",
                                "half_life",
                            ],
                        ]

                        # require no beta functions
                        u = sol_u(t, uu0, alpha, gamma)
                        s = None  # sol_s(t, su0, uu0, 0, 1, gamma)
                        w = sol_u(t, ul0, 0, gamma)
                        l = None  # sol_s(t, 0, 0, alpha, 1, gamma)
                        title_ = ["(unlabeled)", "(labeled)"]
                        if true_param_prefix is not None:
                            true_alpha, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )
                            true_u = sol_u(t, uu0, true_alpha, true_gamma)
                            true_w = sol_u(t, ul0, 0, true_gamma)

                            true_p = np.vstack((true_u, true_w))

                        Obs, Pred = np.vstack((uu, ul)), np.vstack((u, w))

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure unlabled and labeled are in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if true_param_prefix is not None and j == 0:
                            if has_splicing:
                                ax.text(
                                    0.75,
                                    0.50,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\beta$"
                                    + ": {0:.2f}; ".format(true_beta)
                                    + r"$\hat \beta$"
                                    + ": {0:.2f} \n".format(beta)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                            else:
                                ax.text(
                                    0.75,
                                    0.50,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )

                        ax.boxplot(
                            x=[Obs[j][T == std] for std in T_uniq],
                            positions=T_uniq,
                            widths=boxwidth,
                            showfliers=False,
                            showmeans=True,
                        )
                        ax.plot(t, Pred[j], "k--")
                        if true_param_prefix is not None:
                            ax.plot(t, true_p[j], "r--")
                        if j == sub_plot_n - 1:
                            ax.text(
                                0.8,
                                0.8,
                                r"$t_{1/2} = $" + "{0:.2f}".format(half_life) + unit[0],
                                ha="right",
                                va="top",
                                transform=ax.transAxes,
                            )
                        ax.set_xlabel("time (" + unit + ")")
                        if y_log_scale:
                            ax.set_yscale("log")
                        if log_unnormalized:
                            ax.set_ylabel("Expression (log)")
                        else:
                            ax.set_ylabel("Expression")
                        ax.set_title(gene_name + " " + title_[j])
                elif experiment_type == "kin":
                    if model == 'deterministic':
                        logLL = valid_adata.var.loc[valid_gene_names, prefix + 'logLL']
                        alpha, beta, gamma, half_life = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "beta",
                                prefix + "gamma",
                                "half_life",
                            ],
                        ]
                        est_params = [alpha, beta, gamma]
                        gs = plot_kin_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
                                     t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
                                     grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
                                     true_param_prefix, true_params, est_params,
                                     show_variance, show_kin_parameters, )

                    elif model == 'stochastic':
                        if has_splicing:
                            pass
                        else:
                            pass
                    elif model == 'mixture':
                        if has_splicing:
                            pass
                        else:
                            pass
                    elif model == 'mixture_deterministic_stochastic':
                        if has_splicing:
                            pass
                        else:
                            pass
                    elif model == 'mixture_stochastic_stochastic':
                        if has_splicing:
                            pass
                        else:
                            pass
                    else:
                        if has_splicing:
                            layers = (
                                ["X_uu", "X_ul", "X_su", "X_sl"]
                                if "X_ul" in valid_adata.layers.keys()
                                else ["uu", "ul", "su", "sl"]
                            )
                            uu, ul, su, sl = (
                                valid_adata[:, gene_name].layers[layers[0]],
                                valid_adata[:, gene_name].layers[layers[1]],
                                valid_adata[:, gene_name].layers[layers[2]],
                                valid_adata[:, gene_name].layers[layers[3]],
                            )
                            uu, ul, su, sl = (
                                (
                                    uu.toarray().squeeze(),
                                    ul.toarray().squeeze(),
                                    su.toarray().squeeze(),
                                    sl.toarray().squeeze(),
                                )
                                if issparse(uu)
                                else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                            )

                            if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                                uu, ul, su, sl = (
                                    np.log(uu + 1),
                                    np.log(ul + 1),
                                    np.log(su + 1),
                                    np.log(sl + 1),
                                )

                            alpha, beta, gamma, uu0, su0 = valid_adata.var.loc[
                                gene_name,
                                [
                                    prefix + "alpha",
                                    prefix + "beta",
                                    prefix + "gamma",
                                    prefix + "uu0",
                                    prefix + "su0",
                                ],
                            ]
                            # $u$ - unlabeled, unspliced
                            # $s$ - unlabeled, spliced
                            # $w$ - labeled, unspliced
                            # $l$ - labeled, spliced
                            #
                            beta = sys.float_info.epsilon if beta == 0 else beta
                            gamma = sys.float_info.epsilon if gamma == 0 else gamma
                            u = sol_u(t, uu0, 0, beta)
                            s = sol_s(t, su0, uu0, 0, beta, gamma)
                            w = sol_u(t, 0, alpha, beta)
                            l = sol_s(t, 0, 0, alpha, beta, gamma)
                            if true_param_prefix is not None:
                                true_alpha, true_beta, true_gamma = (
                                    valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                    if true_param_prefix + "alpha" in valid_adata.var_keys()
                                    else -np.inf,
                                    valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                                    if true_param_prefix + "beta" in valid_adata.var_keys()
                                    else -np.inf,
                                    valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                    if true_param_prefix + "gamma" in valid_adata.var_keys()
                                    else -np.inf,
                                )
                                true_u = sol_u(t, uu0, 0, true_beta)
                                true_s = sol_s(t, su0, uu0, 0, true_beta, true_gamma)
                                true_w = sol_u(t, 0, true_alpha, true_beta)
                                true_l = sol_s(t, 0, 0, true_alpha, true_beta, true_gamma)

                                true_p = np.vstack((true_u, true_w, true_s, true_l))

                            title_ = [
                                "(unspliced unlabeled)",
                                "(unspliced labeled)",
                                "(spliced unlabeled)",
                                "(spliced labeled)",
                            ]

                            Obs, Pred = np.vstack((uu, ul, su, sl)), np.vstack((u, w, s, l))
                        else:
                            layers = (
                                ["X_new", "X_total"]
                                if "X_new" in valid_adata.layers.keys()
                                else ["new", "total"]
                            )
                            uu, ul = (
                                valid_adata[:, gene_name].layers[layers[1]]
                                - valid_adata[:, gene_name].layers[layers[0]],
                                valid_adata[:, gene_name].layers[layers[0]],
                            )
                            uu, ul = (
                                (uu.toarray().squeeze(), ul.toarray().squeeze())
                                if issparse(uu)
                                else (uu.squeeze(), ul.squeeze())
                            )

                            if log_unnormalized and layers == ["new", "total"]:
                                uu, ul = np.log(uu + 1), np.log(ul + 1)

                            alpha, gamma, uu0 = valid_adata.var.loc[
                                gene_name, [prefix + "alpha", prefix + "gamma", prefix + "uu0"]
                            ]

                            # require no beta functions
                            u = sol_u(t, uu0, 0, gamma)
                            s = None  # sol_s(t, su0, uu0, 0, 1, gamma)
                            w = sol_u(t, 0, alpha, gamma)
                            l = None  # sol_s(t, 0, 0, alpha, 1, gamma)
                            if true_param_prefix is not None:
                                true_alpha, true_gamma = (
                                    valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                    if true_param_prefix + "alpha" in valid_adata.var_keys()
                                    else -np.inf,
                                    valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                    if true_param_prefix + "gamma" in valid_adata.var_keys()
                                    else -np.inf,
                                )
                                true_u = sol_u(t, uu0, 0, true_gamma)
                                true_w = sol_u(t, 0, true_alpha, true_gamma)

                                true_p = np.vstack((true_u, true_w))

                            title_ = ["(unlabeled)", "(labeled)"]

                            Obs, Pred = np.vstack((uu, ul)), np.vstack((u, w))

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure unlabled and labeled are in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if true_param_prefix is not None and j == 0:
                            if has_splicing:
                                ax.text(
                                    0.75,
                                    0.90,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\beta$"
                                    + ": {0:.2f}; ".format(true_beta)
                                    + r"$\hat \beta$"
                                    + ": {0:.2f} \n".format(beta)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )
                            else:
                                ax.text(
                                    0.75,
                                    0.90,
                                    r"$\alpha$"
                                    + ": {0:.2f}; ".format(true_alpha)
                                    + r"$\hat \alpha$"
                                    + ": {0:.2f} \n".format(alpha)
                                    + r"$\gamma$"
                                    + ": {0:.2f}; ".format(true_gamma)
                                    + r"$\hat \gamma$"
                                    + ": {0:.2f} \n".format(gamma),
                                    ha="right",
                                    va="top",
                                    transform=ax.transAxes,
                                )

                        ax.boxplot(
                            x=[Obs[j][T == std] for std in T_uniq],
                            positions=T_uniq,
                            widths=boxwidth,
                            showfliers=False,
                            showmeans=True,
                        )
                        ax.plot(t, Pred[j], "k--")
                        if true_param_prefix is not None:
                            ax.plot(t, true_p[j], "k--")
                        ax.set_xlabel("time (" + unit + ")")
                        if y_log_scale:
                            ax.set_yscale("log")
                        if log_unnormalized:
                            ax.set_ylabel("Expression (log)")
                        else:
                            ax.set_ylabel("Expression")
                        ax.set_title(gene_name + " " + title_[j])
                elif experiment_type == "one_shot":
                    if has_splicing:
                        layers = (
                            ["X_uu", "X_ul", "X_su", "X_sl"]
                            if "X_ul" in valid_adata.layers.keys()
                            else ["uu", "ul", "su", "sl"]
                        )
                        uu, ul, su, sl = (
                            valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[1]],
                            valid_adata[:, gene_name].layers[layers[2]],
                            valid_adata[:, gene_name].layers[layers[3]],
                        )
                        uu, ul, su, sl = (
                            (
                                uu.toarray().squeeze(),
                                ul.toarray().squeeze(),
                                su.toarray().squeeze(),
                                sl.toarray().squeeze(),
                            )
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                        )

                        if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                            uu, ul, su, sl = (
                                np.log(uu + 1),
                                np.log(ul + 1),
                                np.log(su + 1),
                                np.log(sl + 1),
                            )

                        alpha, beta, gamma, U0, S0 = valid_adata.var.loc[
                            gene_name,
                            [
                                prefix + "alpha",
                                prefix + "beta",
                                prefix + "gamma",
                                prefix + "U0",
                                prefix + "S0",
                            ],
                        ]
                        # $u$ - unlabeled, unspliced
                        # $s$ - unlabeled, spliced
                        # $w$ - labeled, unspliced
                        # $l$ - labeled, spliced
                        #
                        beta = sys.float_info.epsilon if beta == 0 else beta
                        gamma = sys.float_info.epsilon if gamma == 0 else gamma
                        U_sol = sol_u(t, U0, 0, beta)
                        S_sol = sol_u(t, S0, 0, gamma)
                        l = sol_u(t, 0, alpha, beta) + sol_s(t, 0, 0, alpha, beta, gamma)
                        L = sl + ul
                        if true_param_prefix is not None:
                            true_alpha, true_beta, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
                                if true_param_prefix + "beta" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )
                            true_l = sol_u(t, 0, true_alpha, true_beta) + sol_s(
                                t, 0, 0, true_alpha, true_beta, true_gamma
                            )

                        title_ = ["labeled"]
                    else:
                        layers = (
                            ["X_new", "X_total"]
                            if "X_new" in valid_adata.layers.keys()
                            else ["new", "total"]
                        )
                        uu, ul = (
                            valid_adata[:, gene_name].layers[layers[1]]
                            - valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[0]],
                        )
                        uu, ul = (
                            (uu.toarray().squeeze(), ul.toarray().squeeze())
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze())
                        )

                        if log_unnormalized and layers == ["new", "total"]:
                            uu, ul = np.log(uu + 1), np.log(ul + 1)

                        alpha, gamma, total0 = valid_adata.var.loc[
                            gene_name,
                            [prefix + "alpha", prefix + "gamma", prefix + "total0"],
                        ]

                        # require no beta functions
                        old = sol_u(t, total0, 0, gamma)
                        s = None  # sol_s(t, su0, uu0, 0, 1, gamma)
                        w = None
                        l = sol_u(t, 0, alpha, gamma)  # sol_s(t, 0, 0, alpha, 1, gamma)
                        L = ul
                        if true_param_prefix is not None:
                            true_alpha, true_gamma = (
                                valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
                                if true_param_prefix + "alpha" in valid_adata.var_keys()
                                else -np.inf,
                                valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
                                if true_param_prefix + "gamma" in valid_adata.var_keys()
                                else -np.inf,
                            )
                            true_l = sol_u(
                                t, 0, true_alpha, true_gamma
                            )  # sol_s(t, 0, 0, alpha, 1, gamma)

                        title_ = ["labeled"]

                    Obs, Pred = np.hstack((np.zeros(L.shape), L)), np.hstack(l)
                    if true_param_prefix is not None:
                        true_p = np.hstack(true_l)

                    row_ind = int(
                        np.floor(i / ncols)
                    )  # make sure unlabled and labeled are in the same column.

                    col_loc = (row_ind * sub_plot_n) * ncols * grp_len + \
                              (i % ncols - 1) * grp_len + 1
                    row_i, col_i = np.where(fig_mat == col_loc)
                    ax = plt.subplot(
                        gs[col_loc]
                    ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                    if true_param_prefix is not None:
                        if has_splicing:
                            ax.text(
                                0.05,
                                0.90,
                                r"$\alpha$ "
                                + ": {0:.2f}; ".format(true_alpha)
                                + r"$\hat \alpha$"
                                + ": {0:.2f} \n".format(alpha)
                                + r"$\beta$"
                                + ": {0:.2f}; ".format(true_beta)
                                + r"$\hat \beta$"
                                + ": {0:.2f} \n".format(beta)
                                + r"$\gamma$"
                                + ": {0:.2f}; ".format(true_gamma)
                                + r"$\hat \gamma$"
                                + ": {0:.2f} \n".format(gamma),
                                ha="left",
                                va="top",
                                transform=ax.transAxes,
                            )
                        else:
                            ax.text(
                                0.05,
                                0.90,
                                r"$\alpha$"
                                + ": {0:.2f}; ".format(true_alpha)
                                + r"$\hat \alpha$"
                                + ": {0:.2f} \n".format(alpha)
                                + r"$\gamma$"
                                + ": {0:.2f}; ".format(true_gamma)
                                + r"$\hat \gamma$"
                                + ": {0:.2f} \n".format(gamma),
                                ha="left",
                                va="top",
                                transform=ax.transAxes,
                            )
                    ax.boxplot(
                        x=[
                            Obs[np.hstack((np.zeros_like(T), T)) == std]
                            for std in [0, T_uniq[0]]
                        ],
                        positions=[0, T_uniq[0]],
                        widths=boxwidth,
                        showfliers=False,
                        showmeans=True,
                    )
                    ax.plot(t, Pred, "k--")
                    if true_param_prefix is not None:
                        ax.plot(t, true_p, "r--")
                    ax.set_xlabel("time (" + unit + ")")
                    if y_log_scale:
                        ax.set_yscale("log")
                    if log_unnormalized:
                        ax.set_ylabel("Expression (log)")
                    else:
                        ax.set_ylabel("Expression")
                    ax.set_title(gene_name + " " + title_[0])
                elif experiment_type == "mix_std_stm":
                    if has_splicing:
                        layers = (
                            ["X_uu", "X_ul", "X_su", "X_sl"]
                            if "X_ul" in valid_adata.layers.keys()
                            else ["uu", "ul", "su", "sl"]
                        )
                        uu, ul, su, sl = (
                            valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[1]],
                            valid_adata[:, gene_name].layers[layers[2]],
                            valid_adata[:, gene_name].layers[layers[3]],
                        )
                        uu, ul, su, sl = (
                            (
                                uu.toarray().squeeze(),
                                ul.toarray().squeeze(),
                                su.toarray().squeeze(),
                                sl.toarray().squeeze(),
                            )
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
                        )

                        if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
                            uu, ul, su, sl = (
                                np.log(uu + 1),
                                np.log(ul + 1),
                                np.log(su + 1),
                                np.log(sl + 1),
                            )

                        beta, gamma, alpha_std = valid_adata.var.loc[
                            gene_name,
                            [prefix + "beta", prefix + "gamma", prefix + "alpha_std"],
                        ]
                        alpha_stm = valid_adata[:, gene_name].varm[prefix + "alpha"].flatten()[1:]
                        alpha_stm0, k, _ = solve_first_order_deg(T_uniq[1:], alpha_stm)

                        # $u$ - unlabeled, unspliced
                        # $s$ - unlabeled, spliced
                        # $w$ - labeled, unspliced
                        # $l$ - labeled, spliced
                        #
                        # calculate labeled unspliced and spliced mRNA amount
                        u1, s1, u1_, s1_ = (
                            np.zeros(len(t) - 1),
                            np.zeros(len(t) - 1),
                            np.zeros(len(T_uniq) - 1),
                            np.zeros(len(T_uniq) - 1),
                        )
                        for ind in np.arange(1, len(t)):
                            t_i = t[ind]
                            u0 = sol_u(np.max(t) - t_i, 0, alpha_std, beta)
                            alpha_stm_t_i = alpha_stm0 * np.exp(-k * t_i)
                            u1[ind - 1], s1[ind - 1] = (
                                sol_u(t_i, u0, alpha_stm_t_i, beta),
                                sol_u(np.max(t), 0, beta, gamma),
                            )
                        for ind in np.arange(1, len(T_uniq)):
                            t_i = T_uniq[ind]
                            u0 = sol_u(np.max(T_uniq) - t_i, 0, alpha_std, beta)
                            alpha_stm_t_i = alpha_stm[ind - 1]
                            u1_[ind - 1], s1_[ind - 1] = (
                                sol_u(t_i, u0, alpha_stm_t_i, beta),
                                sol_u(np.max(T_uniq), 0, beta, gamma),
                            )

                        Obs, Pred, Pred_ = (
                            np.vstack((ul, sl, uu, su)),
                            np.vstack((u1.reshape(1, -1), s1.reshape(1, -1))),
                            np.vstack((u1_.reshape(1, -1), s1_.reshape(1, -1))),
                        )
                        j_species, title_ = (
                            4,
                            [
                                "unspliced labeled (new)",
                                "spliced labeled (new)",
                                "unspliced unlabeled (old)",
                                "spliced unlabeled (old)",
                                "alpha (steady state vs. stimulation)",
                            ],
                        )
                    else:
                        layers = (
                            ["X_new", "X_total"]
                            if "X_new" in valid_adata.layers.keys()
                            else ["new", "total"]
                        )
                        uu, ul = (
                            valid_adata[:, gene_name].layers[layers[1]]
                            - valid_adata[:, gene_name].layers[layers[0]],
                            valid_adata[:, gene_name].layers[layers[0]],
                        )
                        uu, ul = (
                            (uu.toarray().squeeze(), ul.toarray().squeeze())
                            if issparse(uu)
                            else (uu.squeeze(), ul.squeeze())
                        )

                        if log_unnormalized and layers == ["new", "total"]:
                            uu, ul = np.log(uu + 1), np.log(ul + 1)

                        gamma, alpha_std = valid_adata.var.loc[
                            gene_name, [prefix + "gamma", prefix + "alpha_std"]
                        ]
                        alpha_stm = valid_adata[:, gene_name].varm[prefix + "alpha"].flatten()[1:]

                        alpha_stm0, k, _ = solve_first_order_deg(T_uniq[1:], alpha_stm)
                        # require no beta functions
                        u1, u1_ = (
                            np.zeros(len(t) - 1),
                            np.zeros(len(T_uniq) - 1),
                        )  # interpolation or original time point
                        for ind in np.arange(1, len(t)):
                            t_i = t[ind]
                            u0 = sol_u(np.max(t) - t_i, 0, alpha_std, gamma)
                            alpha_stm_t_i = alpha_stm0 * np.exp(-k * t_i)
                            u1[ind - 1] = sol_u(t_i, u0, alpha_stm_t_i, gamma)

                        for ind in np.arange(1, len(T_uniq)):
                            t_i = T_uniq[ind]
                            u0 = sol_u(np.max(T_uniq) - t_i, 0, alpha_std, gamma)
                            alpha_stm_t_i = alpha_stm[ind - 1]
                            u1_[ind - 1] = sol_u(t_i, u0, alpha_stm_t_i, gamma)

                        Obs, Pred, Pred_ = (
                            np.vstack((ul, uu)),
                            np.vstack((u1.reshape(1, -1))),
                            np.vstack((u1_.reshape(1, -1))),
                        )
                        j_species, title_ = (
                            2,
                            [
                                "labeled (new)",
                                "unlabeled (old)",
                                "alpha (steady state vs. stimulation)",
                            ],
                        )

                    group_list = [np.repeat(alpha_std, len(alpha_stm)), alpha_stm]

                    for j in range(sub_plot_n):
                        row_ind = int(
                            np.floor(i / ncols)
                        )  # make sure all related plots for the same gene in the same column.

                        col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
                                  (i % ncols - 1) * grp_len + 1
                        row_i, col_i = np.where(fig_mat == col_loc)
                        ax = plt.subplot(
                            gs[col_loc]
                        ) if gene_order == 'column' else \
                            plt.subplot(
                                gs[fig_mat[col_i, row_i]]
                            )
                        if j < j_species / 2:
                            ax.boxplot(
                                x=[Obs[j][T == std] for std in T_uniq[1:]],
                                positions=T_uniq[1:],
                                widths=boxwidth,
                                showfliers=False,
                                showmeans=True,
                            )
                            ax.plot(t[1:], Pred[j], "k--")
                            ax.scatter(T_uniq[1:], Pred_[j], s=20, c="red")
                            ax.set_xlabel("time (" + unit + ")")
                            ax.set_title(gene_name + ": " + title_[j])

                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Expression (log)")
                            else:
                                ax.set_ylabel("Expression")
                        elif j < j_species:
                            ax.boxplot(
                                x=[Obs[j][T == std] for std in T_uniq],
                                positions=T_uniq,
                                widths=boxwidth,
                                showfliers=False,
                                showmeans=True,
                            )
                            ax.set_xlabel("time (" + unit + ")")
                            ax.set_title(gene_name + ": " + title_[j])

                            if y_log_scale:
                                ax.set_yscale("log")
                            if log_unnormalized:
                                ax.set_ylabel("Expression (log)")
                            else:
                                ax.set_ylabel("Expression")

                        else:
                            x = T_uniq[1:]  # the label locations
                            group_width = barwidth / 2
                            bar_coord, group_name, group_ind = (
                                [-1, 1],
                                ["steady state", "stimulation"],
                                0,
                            )

                            for group_ind in range(len(group_list)):
                                cur_group = group_list[group_ind]
                                ax.bar(
                                    x + bar_coord[group_ind] * group_width,
                                    cur_group,
                                    barwidth,
                                    label=group_name[group_ind],
                                )
                                # Add gene name, experimental type, etc.
                                ax.set_xlabel("time (" + unit + ")")
                                ax.set_ylabel("alpha (translation rate)")
                                ax.set_xticks(x)
                                ax.set_xticklabels(x)
                                group_ind += 1
                            ax.legend()

                        ax.set_xlabel("time (" + unit + ")")
                        ax.set_title(gene_name + ": " + title_[j])
                elif experiment_type == "multi_time_series":
                    pass  # group by different groups
                elif experiment_type == "coassay":
                    pass  # show protein velocity (steady state and the Gamma distribution model)
    g.autofmt_xdate(rotation=-30, ha='right')
    if save_show_or_return == "save":
        s_kwargs = {"path": None, "prefix": 'dynamics', "dpi": None,
                    "ext": 'pdf', "transparent": True, "close": True, "verbose": True}
        s_kwargs = update_dict(s_kwargs, save_kwargs)
        save_fig(**s_kwargs)
    elif save_show_or_return == "show":
        plt.tight_layout()
        plt.show()
    elif save_show_or_return == "return":
        return g


def dynamics_(
        adata,
        gene_names,
        color,
        dims=[0, 1],
        current_layer="spliced",
        use_raw=False,
        Vkey="S",
        Ekey="spliced",
        basis="umap",
        mode="all",
        cmap=None,
        gs=None,
        **kwargs
):
    """

    Parameters
    ----------
    adata
    basis
    mode: `str` (default: all)
        Support mode includes: phase, expression, velocity, all

    Returns
    -------

    """

    import matplotlib.pyplot as plt

    genes = list(set(gene_names).intersection(adata.var.index))
    for i, gn in enumerate(genes):
        ax = plt.subplot(gs[i * 3])
        try:
            ix = np.where(adata.var["Gene"] == gn)[0][0]
        except:
            continue

        scatters(
            adata,
            gene_names,
            color,
            dims=[0, 1],
            current_layer="spliced",
            use_raw=False,
            Vkey="S",
            Ekey="spliced",
            basis="umap",
            mode="all",
            cmap=None,
            gs=None,
            **kwargs
        )

        scatters(
            adata,
            gene_names,
            color,
            dims=[0, 1],
            current_layer="spliced",
            use_raw=False,
            Vkey="S",
            Ekey="spliced",
            basis="umap",
            mode="all",
            cmap=None,
            gs=None,
            **kwargs
        )

        scatters(
            adata,
            gene_names,
            color,
            dims=[0, 1],
            current_layer="spliced",
            use_raw=False,
            Vkey="S",
            Ekey="spliced",
            basis="umap",
            mode="all",
            cmap=None,
            gs=None,
            **kwargs
        )

    plt.tight_layout()



Draw the phase portrait, expression values , velocity on the low dimensional embedding.     Note that this function allows to manually set the theme, cmap, color_key and color_key_cmap     for the phase portrait, expression and velocity subplots. When the background is 'black',     the default themes for each of those subplots are  ["glasbey_dark", "inferno", "div_blue_black_red"],     respectively. When the background is 'black', the default themes are  "glasbey_white", "viridis",     "div_blue_red".      Parameters     ----------         adata: :class:`~anndata.AnnData`             an Annodata object         genes: `list`             A list of gene names that are going to be visualized.         x: `int` (default: `0`)                 The column index of the low dimensional embedding for the x-axis         y: `int` (default: `1`)                 The column index of the low dimensional embedding for the y-axis         pointsize: `None` or `float` (default: None)                 The scale of the point size. Actual point cell size is calculated as `2500.0 / np.sqrt(adata.shape[0]) *                 pointsize`         vkey: `string` or None (default: None)             Which velocity key used for visualizing the magnitude of velocity. Can be either velocity in the layers slot             or the keys in the obsm slot.         ekey: `str` or None (default: `None`)             The layer of data to represent the gene expression level.         basis: `string` (default: umap)             Which low dimensional embedding will be used to visualize the cell.         log1p: `bool` (default: `True`)             Whether to log1p transform the expression so that visualization can be robust to extreme values.         color: `string` (default: 'cell_cycle_phase')             Which group will be used to color cells, only used for the phase portrait because the other two plots are             colored by the velocity magnitude or the gene expression value, respectively.         use_smoothed: `bool` (default: `True`)             Whether to use smoothed 1/2-nd moments as gene expression for the first and third columns. This is useful             for checking the confidence of transition genes. For example, you may see a very nice linear pattern for             some genes with the smoothed expression but this could just be an artificially generated when the number of             expressed cells is very low. This raises red flags for the quality of the velocity values we learned for             those genes. And we recommend to set the higher values (for example, 10% of all cells) for `min_cell_s`,             `min_cell_u` or `shared_count` of the `fg_kwargs` argument of the dyn.pl.receipe_monocle. Note that this is             often related to the small single cell datasets (like plate-based scRNA-seq or scSLAM-seq/NASC-seq, etc).         highlights: `list` (default: None)             Which color group will be highlighted. if highligts is a list of lists - each list is relate to each color             element.         discrete_continous_div_themes: `list[str, str, str]` (optional, default None)             The discrete, continous and divergent color themes to use for plotting. The description for each element in             the list is as following.             A small set of predefined themes are provided which have relatively good aesthetics. Available themes are:                * 'blue'                * 'red'                * 'green'                * 'inferno'                * 'fire'                * 'viridis'                * 'darkblue'                * 'darkred'                * 'darkgreen'         discrete_continous_div_cmap: `list[str, str, str]`  (optional, default 'Blues')             The names of  discrete, continous and divergent matplotlib colormap to use for coloring             or shading points. The description for each element in the list is as following. If no labels or values are             passed this will be used for shading points according to density (largely only of relevance for very large             datasets). If values are passed this will be used for shading according the value. Note that if theme is             passed then this value will be overridden by the corresponding option of the theme.         discrete_continous_div_color_key: `list[dict or array,, dict or array,, dict or array,]` (default [None, None,             None]).             The description for each element in the list is as following. The shape (n_categories) (optional, default             None). A list to assign discrete, continous and divergent colors to categoricals. This can either be an             explicit dict mapping labels to colors (as strings of form '#RRGGBB'), or an array like object providing one             color for each distinct category being provided in ``labels``. Either way this mapping will be used to color             points according to the label. Note that if theme is passed then this value will be overridden by the             corresponding option of the theme.         discrete_continous_div_color_key_cmap: `list[str, str, str]`, (optional, default 'Spectral')             The names of discrete, continous and divergent matplotlib colormap to use for categorical coloring.             The description for each element in the list is as following. If an explicit ``color_key`` is not given a             color mapping for categories can be generated from the label list and selecting a matching list of colors             from the given colormap. Note that if theme is passed then this value will be overridden by the corresponding             option of the theme.         figsize: `None` or `[float, float]` (default: None)                 The width and height of each panel in the figure.         ncols: `None` or `int` (default: None)         ncol: `None` or `int` (default: None)                 Number of columns in each facet grid.         legend: `str` (default: `on data`)                 Where to put the legend.  Legend is drawn by seaborn with “brief” mode, numeric hue and size variables                 will be represented with a sample of evenly spaced values. By default legend is drawn on top of cells.         show_quiver: `bool` (default: False)             Whether to show the quiver plot. If velocity for x component (corresponds to either spliced, total RNA,             protein, etc) or y component (corresponds to either unspliced, new RNA, protein, etc) are both calculated,             quiver represents velocity for both components otherwise the uncalculated component (usually y component)             will be set to be 0.         quiver_size: `float` or None (default: None)             The size of quiver. If None, we will use set quiver_size to be 1. Note that quiver quiver_size is used to             calculate the head_width (10 x quiver_size), head_length (12 x quiver_size) and headaxislength (8 x             quiver_size) of the quiver. This is done via the `default_quiver_args` function which also calculate the             scale of the quiver (1 / quiver_length).         quiver_length: `float` or None (default: None)             The length of quiver. The quiver length which will be used to calculate scale of quiver. Note that befoe             applying `default_quiver_args` velocity values are first rescaled via the quiver_autoscaler function. Scale             of quiver indicates the nuumber of data units per arrow length unit, e.g., m/s per plot width; a smaller             scale parameter makes the arrow longer.         q_kwargs_dict: `dict` (default: {})             The dictionary of the quiver arguments. The default setting of quiver argument is identical to that used in             the cell_wise_velocity and grid_velocity.         show_arrowed_spines: bool or None (optional, default None)             Whether to show a pair of arrowed spines represeenting the basis of the scatter is currently using. If None,             only the first panel in the expression / velocity plot will have the arrowed spine.         save_show_or_return: {'show', 'save', 'return'} (default: `show`)             Whether to save, show or return the figure.         save_kwargs: `dict` (default: `{}`)             A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig             function will use the {"path": None, "prefix": 'phase_portraits', "dpi": None, "ext": 'pdf', "transparent":             True, "close": True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly             modify those keys according to your needs.         **kwargs:             Additional parameters that will be passed to plt.scatter function      Returns     -------         A matplotlib plot that shows 1) the phase portrait of each category used in velocity embedding, cells' low         dimensional embedding, colored either by 2) the gene expression level or 3) the velocity magnitude values. # from matplotlib.colors import DivergingNorm  # TwoSlopeNorm # (0, 0, 0, 1) # div_scatter_kwargs.update({"norm": DivergingNorm(vcenter=0)}) # avoid object for dtype in the gamma column https://stackoverflow.com/questions/40809503/python-numpy-typeerror-ufunc-isfinite-not-supported-for-the-input-types # idx = [adata.var.index.to_list().index(i) for i in genes] # df = pd.DataFrame({"uu": uu.flatten(), "ul": ul.flatten(), "su": su.flatten(), "sl": sl.flatten(), "P": P.flatten(), #                    'gene': genes * n_cells, 'prediction': np.tile(gamma, n_cells) * uu.flatten() + #                     np.tile(velocity_offset, n_cells), "velocity": genes * n_cells}, index=range(n_cells * n_genes)) # , dpi=160 # , dpi=160 # the following code is inspired by https://github.com/velocyto-team/velocyto-notebooks/blob/master/python/DentateGyrus.ipynb # add quiver: # sns.despline() # limit = np.nanmax( #     np.abs(np.nanpercentile(V_vec, [1, 99])) # )  # upper and lowe limit / saturation # # V_vec = V_vec + limit  # that is: tmp_colorandum - (-limit) # V_vec = V_vec / (2 * limit)  # that is: tmp_colorandum / (limit - (-limit)) # V_vec = np.clip(V_vec, 0, 1) # add quiver: # sns.despline() # upper and lowe limit / saturation # that is: tmp_colorandum - (-limit) # that is: tmp_colorandum / (limit - (-limit)) Plot the data and fitting of different metabolic labeling experiments.     Note that if non-smoothed data was used for kinetic fitting, you often won't see boxplot     but only the triangles for the mean values. This is because the raw data has a lot dropout,     thus the median is outside of the whiskers of the boxplot and the boxplot is then not drawn     by default.      Parameters     ----------         adata: :class:`~anndata.AnnData`             an Annodata object.         genes: list of `str`             key for variable or gene names.         unit: `str` (default: `hour`)             The unit of the labeling time, for example, `hours` or `minutes`.         y_log_scale: `bool` (default: `True`)             Whether or not to use log scale for y-axis.         ci: `str` (for example: "95%") or None (default: `None`)             The confidence interval to be drawed for the parameter fitting. Currently not used.         ncols: `int` or None (default: `None`)             The number of columns in the plot.         figsize: `[float, float]` or `(float, float)` or None             The size of the each panel in the figure.         dpi: `float` or None             Figure resolution.         boxwidth: `float`             The width of the box of the boxplot.         barwidth: `float`             The width of the bar of the barplot.         true_param_prefix: `str`             The prefix for the column names of true parameters in the .var attributes. Useful for the simulation data.         show_moms_fit: `bool` (default: `True`)             Whether to show fitting curves associated with the stochastic models, only works for non-deterministic models.         show_variance: `bool` (default: `True`)             Whether to add a boxplot to show the variance at each time point.         show_kin_parameters: `bool` (default: `True`)             Whether to include the estimated kinetic parameter values on the plot.         gene_order: `str` (default: `column`)             The order of genes to present on the figure, either row-major or column major.         font_size_scale: `float` (default: `1`)             A value that will be used for scaling         save_show_or_return: {'show', 'save', 'return'} (default: `show`)             Whether to save, show or return the figure.         save_kwargs: `dict` (default: `{}`)             A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function             will use the {"path": None, "prefix": 'dynamics', "dpi": None, "ext": 'pdf', "transparent": True, "close":             True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys             according to your needs.      Returns     -------         Nothing but plot the figure of the metabolic fitting. # for key, val in adata.uns[uns_key].items(): #     exec(key + '=val') # run the phase_portraits plot # number of subplots for each gene # just the labeled RNA # number of subplots for each gene # just the labeled RNA # each column correspond to one gene # there are bugs when fig is not a symmetric matrix # we need to visualize gene in row-wise mode # kinetic_parameter_ # "la": 1, "si": 0, # "la": 1, "si": 0, # n_mean, n_var = x_data[:2, :], x_data[2:, :] # number of species # only use new key for calculation, so we only have M, V # make sure all related plots for the same gene in the same column. # x=T.values, y= # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10) # ax.scatter(T_uniq, Obs_m[j][i], c='r')  # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10) # # , c='r' # # $u$ - unlabeled, unspliced # $s$ - unlabeled, spliced # $w$ - labeled, unspliced # $l$ - labeled, spliced # # this should also be estimated # require no beta functions # sol_s(t, su0, uu0, 0, 1, gamma) # sol_s(t, 0, 0, alpha, 1, gamma) # make sure unlabled and labeled are in the same column. # $u$ - unlabeled, unspliced # $s$ - unlabeled, spliced # $w$ - labeled, unspliced # $l$ - labeled, spliced # # require no beta functions # sol_s(t, su0, uu0, 0, 1, gamma) # sol_s(t, 0, 0, alpha, 1, gamma) # make sure unlabled and labeled are in the same column. # $u$ - unlabeled, unspliced # $s$ - unlabeled, spliced # $w$ - labeled, unspliced # $l$ - labeled, spliced # # require no beta functions # sol_s(t, su0, uu0, 0, 1, gamma) # sol_s(t, 0, 0, alpha, 1, gamma) # sol_s(t, 0, 0, alpha, 1, gamma) # make sure unlabled and labeled are in the same column. # $u$ - unlabeled, unspliced # $s$ - unlabeled, spliced # $w$ - labeled, unspliced # $l$ - labeled, spliced # # calculate labeled unspliced and spliced mRNA amount # require no beta functions # interpolation or original time point # make sure all related plots for the same gene in the same column. # the label locations # Add gene name, experimental type, etc. # group by different groups # show protein velocity (steady state and the Gamma distribution model) Parameters     ----------     adata     basis     mode: `str` (default: all)         Support mode includes: phase, expression, velocity, all      Returns     -------