@@ -6,11 +6,11 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v7
       - name: Setup submodule
         run: git submodule update --init --recursive
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v10.0.1
       - name: Install dependencies
         run: uv sync
       - name: Lint with ruff
 
@@ -1,6 +1,6 @@
 """This file is to collect those lineages that have the same condition, and have 3 or greater number of cells in their lineages."""
 
-from .import_lineage import MCF10A, import_taxol
+from .import_lineage import MCF10A
 from .LineageInputOutput import import_exp_data
 from .LineageTree import LineageTree
 from .states.StateDistributionGamma import StateDistribution as StDist
@@ -153,15 +153,3 @@ def load_replicate_groups(replicate_file_groups: list[list[str]]):
 AllLapatinib = [Lapatinib_Control + Gemcitabine_Control, Lapt25uM, Lapt50uM, Lap250uM]
 AllGemcitabine = [Lapatinib_Control + Gemcitabine_Control, Gem5uM, Gem10uM, Gem30uM]
 GFs = [pbs, egf, hgf, osm]
-
-##########################################################
-# NEW PACLITAXEL DATA
-untreated, taxol_05, taxol_1, taxol_15, taxol_2, taxol_25, taxol_3, taxol_4 = import_taxol()
-# 555, 1119, 486, 382, 272, 365, 246, 266
-untreated_taxol = [LineageTree(list_cells, E) for list_cells in untreated]  # 555 cells
-Taxol_1 = [LineageTree(list_cells, E) for list_cells in taxol_1]  # 486 cells
-Taxol_2 = [LineageTree(list_cells, E) for list_cells in taxol_2]  # 272 cells
-Taxol_3 = [LineageTree(list_cells, E) for list_cells in taxol_3]  # 246 cells
-Taxol_4 = [LineageTree(list_cells, E) for list_cells in taxol_4]  # 266 cells
-
-taxols = [untreated_taxol, Taxol_1, Taxol_2, Taxol_4]
@@ -0,0 +1,104 @@
+"""To plot population level of lapatinib from the lineage trees."""
+
+import random
+
+import numpy as np
+
+from ..Lineage_collections import AllGemcitabine, AllLapatinib
+from .common import getSetup
+
+
+def makeFigure():
+    """
+    Makes figure 1.
+    """
+    # Get list of axis objects
+    ax, f = getSetup((10, 6), (2, 4))
+
+    lpt = ["Lapatinib, 25", "Lapatinib, 50", "Lapatinib 250"]
+    gmc = ["Gemcitabine, 5", "Gemcitabine, 10", "Gemcitabine, 30"]
+    plot_each_drug(f, ax[:4], AllLapatinib, lpt)
+    plot_each_drug(f, ax[4:], AllGemcitabine, gmc)
+    return f
+
+
+def leaves_to_root(lin, ts):
+    """A function to traverse from root to leaves
+    by randomly selecting a daughter cell at each generation forward,
+    and calculate the time-series counts at G1 and S/G2."""
+    counts = np.zeros((ts.size, 2))  # G1 and S/G2
+
+    t_cur = 0.0
+
+    # randomly choose one of leaf cells.
+    leaves_idx = lin.leaves_idx
+    lineage = lin.output_lineage
+    random_leaf_idx = random.choice(leaves_idx)
+
+    cells = [lineage[random_leaf_idx]]
+
+    # traverse back to root from the randomly chosen leaf
+    for c in cells:
+        if c.parent is not None:
+            cells.append(c.parent)
+
+    cells.reverse()  # order from root to leaf
+
+    for ii, cell in enumerate(cells):  # the number of generations
+        for phase in range(2):
+            gamma_obs = np.nan_to_num(cell.obs[phase + 2])
+            idx = (ts > t_cur) & (ts < t_cur + gamma_obs)
+            counts[idx, phase] += 2 ** (ii + 1)
+            t_cur += gamma_obs
+    return counts
+
+
+def plot_each_drug(f, ax, all_drug, name_list):
+    control = all_drug[0]
+    c1 = all_drug[1]
+    c2 = all_drug[2]
+    c3 = all_drug[3]
+
+    ts = np.arange(96, step=0.5)
+
+    Gs = leaves_to_root(control[0], ts)
+    for lineage in control[1:]:
+        Gs += leaves_to_root(lineage, ts)
+    ax[0].plot(Gs[:, 0], label="G1")
+    ax[0].plot(Gs[:, 1], label="SG2")
+    ax[0].set_title("Untreated")
+    ax[0].set_xlabel("Time [0.5hr]")
+    ax[0].set_ylabel("Cell counts")
+    ax[0].legend()
+
+    Gs1 = leaves_to_root(c1[0], ts)
+    for lineage in c1[1:]:
+        Gs1 += leaves_to_root(lineage, ts)
+    ax[1].plot(Gs1[:, 0], label="G1")
+    ax[1].plot(Gs1[:, 1], label="SG2")
+    ax[1].set_title(name_list[0] + " nM")
+    ax[1].set_xlabel("Time [0.5hr]")
+    ax[1].set_ylabel("Cell counts")
+    ax[1].legend()
+
+    Gs2 = leaves_to_root(c2[0], ts)
+    for lineage in c2[1:]:
+        Gs2 += leaves_to_root(lineage, ts)
+    ax[2].plot(Gs2[:, 0], label="G1")
+    ax[2].plot(Gs2[:, 1], label="SG2")
+    ax[2].set_title(name_list[1] + " nM")
+    ax[2].set_xlabel("Time [0.5hr]")
+    ax[2].set_ylabel("Cell counts")
+    ax[2].legend()
+
+    Gs3 = leaves_to_root(c3[0], ts)
+    for lineage in c3[1:]:
+        Gs3 += leaves_to_root(lineage, ts)
+    ax[3].plot(Gs3[:, 0], label="G1")
+    ax[3].plot(Gs3[:, 1], label="SG2")
+    ax[3].set_title(name_list[2] + " nM")
+    ax[3].set_xlabel("Time [0.5hr]")
+    ax[3].set_ylabel("Cell counts")
+    ax[3].legend()
+
+    f.tight_layout()