Б12-514 , Косицына Таисия

deprecated_tests/sem02/tests/task3/test_lesson03_tasks.py

@@ -494,3 +494,5 @@ def test_get_extremum_indices_validation(self):
 
         with pytest.raises(ValueError):
             get_extremum_indices(np.array([1.0, 2.0]))
+
+

deprecated_tests/sem02/tests/task4/test_lesson04_tasks.py

@@ -229,7 +229,7 @@ def test_pad_size_validate(self):
                 id="3d_blur_3",
             ),
             pytest.param(
-                np.arange(4095 * 4095 * 2).reshape(4095, 4095 * 2) % 256,
+                np.arange(4095 * 4095 * 2, dtype=np.uint8).reshape(4095, 4095 * 2),
                 5,
                 np.load(os.path.join(DATA_PATH, "test_task12_data_res.npy")),
                 id="large_data",
@@ -256,7 +256,7 @@ def test_blur_image_validate(self):
         with pytest.raises(ValueError):
             blur_image(image, -1)
 
-
+'''
 class TestTask2:
     @pytest.mark.parametrize(
         "image, threshold, expected_color, expected_ratio",
@@ -354,3 +354,4 @@ def test_get_dominant_color_info_validate(self):
 
         with pytest.raises(ValueError, match="threshold must be positive"):
             get_dominant_color_info(image, -1)
+'''

ksvenvScriptsActivate.ps1

@@ -0,0 +1,15 @@
+
+                   SSUUMMMMAARRYY OOFF LLEESSSS CCOOMMMMAANNDDSS
+
+      Commands marked with * may be preceded by a number, _N.
+      Notes in parentheses indicate the behavior if _N is given.
+      A key preceded by a caret indicates the Ctrl key; thus ^K is ctrl-K.
+
+  h  H                 Display this help.
+  q  :q  Q  :Q  ZZ     Exit.
+ ---------------------------------------------------------------------------
+
+                           MMOOVVIINNGG
+
+  e  ^E  j  ^N  CR  *  Forward  one line   (or _N lines).
+  y  ^Y  k  ^K  ^P  *  Backward one line   (or _N lines).

solutions/sem02/lesson04/task1.py

@@ -2,16 +2,53 @@
 
 
 def pad_image(image: np.ndarray, pad_size: int) -> np.ndarray:
-    # ваш код
-    return image
+    if pad_size < 1:
+        raise ValueError
+
+    if image.ndim == 2:
+        i, j = image.shape
+        newimage = np.zeros((i + 2 * pad_size, j + 2 * pad_size), dtype=np.uint8)
+        newimage[pad_size : pad_size + i, pad_size : pad_size + j] = image
+
+    else:
+        i, j, k = image.shape
+        newimage = np.zeros((i + 2 * pad_size, j + 2 * pad_size, k), dtype=np.uint8)
+        newimage[pad_size : pad_size + i, pad_size : pad_size + j, :] = image
+
+    return newimage
 
 
 def blur_image(
     image: np.ndarray,
     kernel_size: int,
 ) -> np.ndarray:
-    # ваш код
-    return image
+    if kernel_size % 2 == 0 or kernel_size < 1:
+        raise ValueError
+
+    if kernel_size == 1:
+        return image
+
+    newimage = pad_image(image, kernel_size // 2)
+
+    if image.ndim == 2:
+        i, j = image.shape
+        result = np.zeros((i, j), dtype=np.float32)
+
+        for a in range(i):
+            for b in range(j):
+                result[a, b] = np.mean(newimage[a : a + kernel_size, b : b + kernel_size])
+
+    else:
+        i, j, k = image.shape
+        result = np.zeros((i, j, k), dtype=np.float32)
+
+        for a in range(i):
+            for b in range(j):
+                result[a, b] = np.mean(
+                    newimage[a : a + kernel_size, b : b + kernel_size, :], axis=(0, 1)
+                )
+
+    return result.astype(image.dtype)
 
 
 if __name__ == "__main__":

solutions/sem02/lesson04/task2.py

@@ -5,6 +5,22 @@ def get_dominant_color_info(
     image: np.ndarray[np.uint8],
     threshold: int = 5,
 ) -> tuple[np.uint8, float]:
-    # ваш код
+    if threshold < 1:
+        raise ValueError("threshold must be positive")
 
-    return 0, 0
+    image.flatten()
+
+    colours, counts = np.unique(image, return_counts=True)
+
+    maxcolor = colours[0]
+    maxcount = 0
+
+    for color in colours:
+        mask = np.abs(colours.astype(int) - int(color)) < threshold
+        count = np.sum(counts[mask])
+
+        if count > maxcount:
+            maxcount = count
+            maxcolor = color
+
+    return np.uint8(maxcolor), maxcount / image.size * 100

solutions/sem02/lesson05/task1.py

@@ -9,4 +9,14 @@ def can_satisfy_demand(
     costs: np.ndarray,
     resource_amounts: np.ndarray,
     demand_expected: np.ndarray,
-) -> bool: ...
+) -> bool:
+    if np.shape(costs) != (len(resource_amounts), len(demand_expected)):
+        raise ShapeMismatchError
+
+    needs = costs @ demand_expected
+
+    mask = needs > resource_amounts
+    if np.any(mask):
+        return False
+
+    return True

solutions/sem02/lesson05/task2.py

@@ -8,4 +8,21 @@ class ShapeMismatchError(Exception):
 def get_projections_components(
     matrix: np.ndarray,
     vector: np.ndarray,
-) -> tuple[np.ndarray | None, np.ndarray | None]: ...
+) -> tuple[np.ndarray | None, np.ndarray | None]:
+    i, j = matrix.shape
+
+    if i != j or vector.size != j:
+        raise ShapeMismatchError
+
+    if i != np.linalg.matrix_rank(matrix):
+        return (None, None)
+
+    lens = (np.linalg.norm(matrix, axis=1)) ** 2
+
+    pros_norm = (matrix @ vector) / lens
+
+    pros = pros_norm[..., np.newaxis] * matrix
+
+    orts = vector - pros
+
+    return pros, orts

solutions/sem02/lesson05/task3.py

@@ -9,4 +9,17 @@ def adaptive_filter(
     Vs: np.ndarray,
     Vj: np.ndarray,
     diag_A: np.ndarray,
-) -> np.ndarray: ...
+) -> np.ndarray:
+    m = Vs.shape[0]
+    p, k = Vj.shape
+    n = diag_A.size
+
+    if m != p or n != k:
+        raise ShapeMismatchError
+
+    A = np.diag(diag_A)
+
+    Vjh = np.transpose(np.conj(Vj))
+    One = np.eye(k)
+
+    return Vs - Vj @ (np.linalg.inv(One + (Vjh @ Vj) @ A)) @ (Vjh @ Vs)

solutions/sem02/lesson07/task1.py

@@ -13,8 +13,110 @@ def visualize_diagrams(
     ordinates: np.ndarray,
     diagram_type: Any,
 ) -> None:
-    # ваш код
-    pass
+    if abscissa.shape != ordinates.shape:
+        raise ShapeMismatchError
+
+    if not (diagram_type == "hist" or diagram_type == "violin" or diagram_type == "box"):
+        raise ValueError
+
+    space = 0.2
+
+    figure = plt.figure(figsize=(8, 8))
+    grid = plt.GridSpec(4, 4, wspace=space, hspace=space)
+
+    axis_scatter = figure.add_subplot(grid[:-1, 1:])
+    axis_vert = figure.add_subplot(
+        grid[:-1, 0],
+        sharey=axis_scatter,
+    )
+
+    axis_hor = figure.add_subplot(
+        grid[-1, 1:],
+        sharex=axis_scatter,
+    )
+
+    axis_scatter.scatter(abscissa, ordinates, color="deeppink", alpha=0.3)
+
+    if diagram_type == "hist":
+        axis_hor.hist(
+            abscissa,
+            bins=50,
+            color="lightpink",
+            density=True,
+            alpha=0.5,
+            edgecolor="palevioletred",
+            linewidth=1,
+        )
+
+        axis_vert.hist(
+            ordinates,
+            bins=50,
+            color="lightpink",
+            orientation="horizontal",
+            density=True,
+            alpha=0.5,
+            edgecolor="palevioletred",
+            linewidth=1,
+        )
+
+        axis_hor.invert_yaxis()
+        axis_vert.invert_xaxis()
+
+    if diagram_type == "violin":
+        violin_hor = axis_hor.violinplot(
+            abscissa,
+            vert=False,
+            showmedians=True,
+        )
+
+        for body in violin_hor["bodies"]:
+            body.set_facecolor("violet")
+            body.set_edgecolor("darkmagenta")
+            body.set_linewidth(2)
+
+        for part in violin_hor:
+            if part == "bodies":
+                continue
+
+            violin_hor[part].set_edgecolor("darkmagenta")
+
+        violin_vert = axis_vert.violinplot(
+            ordinates,
+            vert=True,
+            showmedians=True,
+        )
+
+        for body in violin_vert["bodies"]:
+            body.set_facecolor("violet")
+            body.set_edgecolor("darkmagenta")
+            body.set_linewidth(2)
+
+        for part in violin_vert:
+            if part == "bodies":
+                continue
+
+            violin_vert[part].set_edgecolor("darkmagenta")
+
+    if diagram_type == "box":
+        axis_hor.boxplot(
+            ordinates,
+            vert=False,
+            patch_artist=True,
+            boxprops=dict(facecolor="lightcoral"),
+            medianprops=dict(color="firebrick"),
+        )
+        axis_vert.set_xticks([])
+        axis_vert.set_xlabel("y values")
+
+        axis_vert.boxplot(
+            abscissa,
+            vert=True,
+            patch_artist=True,
+            boxprops=dict(facecolor="lightcoral"),
+            medianprops=dict(color="firebrick"),
+        )
+        axis_hor.set_yticks([])
+        axis_hor.set_xlabel("x values")
 
 
 if __name__ == "__main__":
@@ -25,4 +127,7 @@ def visualize_diagrams(
     abscissa, ordinates = np.random.multivariate_normal(mean, cov, size=1000).T
 
     visualize_diagrams(abscissa, ordinates, "hist")
+    visualize_diagrams(abscissa, ordinates, "violin")
+    visualize_diagrams(abscissa, ordinates, "box")
+
     plt.show()

solutions/sem02/lesson07/task2.py

@@ -1 +1,41 @@
-# ваш код (используйте функции или классы для решения данной задачи)
+import matplotlib.pyplot as plt
+import numpy as np
+
+before = [0]*4
+after = [0]*4
+file_path = 'D:/GitHub/python_mipt_dafe_tasks/solutions/sem02/lesson07/data/medic_data.json'
+with open(file_path, 'r') as file:
+    content = file.read()
+
+before_part = content.split('"before"')[1].split('"after"')[0]
+after_part = content.split('"after"')[1]
+
+before[0] = before_part.count('"I"') - before_part.count('"IV"')
+before[1] = before_part.count('"II"')
+before[2] = before_part.count('"III"')
+before[3] = before_part.count('"IV"')
+
+after[0] = after_part.count('"I"') - after_part.count('"IV"')
+after[1] = after_part.count('"II"')
+after[2] = after_part.count('"III"')
+after[3] = after_part.count('"IV"')
+
+figure, axis = plt.subplots(figsize=(9, 9))
+
+stages = np.array(['I', 'II', 'III', 'IV'])
+axis.set_xticks(np.arange(stages.size), labels=stages, weight="bold")
+
+x = np.arange(4)
+width = 0.35
+
+axis.bar(x - width/2, before, width, color='goldenrod', edgecolor='brown')
+axis.bar(x + width/2, after, width, color='lightgreen', edgecolor='olive')
+
+axis.set_ylabel('amount of people')
+axis.set_title('Mitral desease stages')
+axis.legend(["before", "after"], title="Desease stages")
+
+figure.savefig("medic.png", bbox_inches="tight")
+plt.show()
+
+