Merge pull request #13 from codingpot/feat-add-method-list

feat: add c.MethodList()

method_list.go

@@ -0,0 +1,36 @@
+package paperswithcode_go
+
+import (
+	"encoding/json"
+	"fmt"
+	"github.com/codingpot/paperswithcode-go/v2/models"
+)
+
+type MethodListParams struct {
+	// Page (default: 1)
+	Page int
+	// ItemsPerPage (default: 50)
+	ItemsPerPage int
+}
+
+func (m MethodListParams) String() string {
+	return fmt.Sprintf("page=%d&items_per_page=%d", m.Page, m.ItemsPerPage)
+}
+
+func (c *Client) MethodList(params MethodListParams) (*models.MethodList, error) {
+	url := c.baseURL + "/methods?" + params.String()
+
+	response, err := c.httpClient.Get(url)
+	if err != nil {
+		return nil, err
+	}
+
+	var listResult models.MethodList
+
+	err = json.NewDecoder(response.Body).Decode(&listResult)
+	if err != nil {
+		return nil, err
+	}
+
+	return &listResult, nil
+}

method_list_test.go

@@ -0,0 +1,64 @@
+package paperswithcode_go
+
+import (
+	"github.com/codingpot/paperswithcode-go/v2/models"
+	"github.com/stretchr/testify/assert"
+	"testing"
+)
+
+func TestClient_MethodList(t *testing.T) {
+	tests := []struct {
+		name    string
+		params  MethodListParams
+		want    *models.MethodList
+		wantErr bool
+	}{
+		{
+			name: "MethodList returns methods",
+			params: MethodListParams{
+				Page:         1,
+				ItemsPerPage: 2,
+			},
+			want: &models.MethodList{
+				Count:    1129,
+				Next:     toPtr("https://paperswithcode.com/api/v1/methods/?items_per_page=2&page=2"),
+				Previous: nil,
+				Results: []*models.Method{
+					{
+						ID: "1cycle",
+						Name: "1cycle",
+						FullName: "1cycle learning rate scheduling policy",
+						Description: "",
+						Paper: toPtr("a-disciplined-approach-to-neural-network"),
+					},
+					{
+						ID: "1d-cnn",
+						Name: "1D CNN",
+						FullName: "1-Dimensional Convolutional Neural Networks",
+						Description: "1D Convolutional Neural Networks are similar to well known and more established 2D Convolutional Neural Networks. 1D Convolutional Neural Networks are used mainly used on text and 1D signals.",
+						Paper: toPtr("convolutional-neural-network-and-rule-based"),
+					},
+				},
+			},
+			wantErr: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			c := NewClient()
+			got, err := c.MethodList(tt.params)
+			if tt.wantErr {
+				assert.Error(t, err)
+			} else {
+				assert.NoError(t, err)
+			}
+
+			assert.Equal(t, tt.want, got)
+		})
+	}
+}
+
+func toPtr(s string) *string {
+	return &s
+}